System and method for message clustering

ABSTRACT

The disclosure describes systems and methods delivering communications associated with delivery conditions in which the occurrence of the delivery condition is determined by monitoring information received from a plurality of sources via multiple communication channels. The message delivery systems allow messages to be delivered to any “Who, What, When, Where” from any “Who, What, When, Where” upon the detection of an occurrence of one or more “Who, What, When, Where” delivery conditions. A message (which may be any data object including text-based messages, audio-based message such as voicemail or other audio such as music or video-based prerecorded messages) is delivered in accordance with delivery conditions based on any available data, including topical, spatial, temporal, and/or social data. Furthermore, because the systems coordinate delivery of messages via multiple communication channels and through multiple devices, the communication channel for delivery of a message may be dynamically determined based on the delivery conditions.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority fromco-pending U.S. patent application Ser. No. 12/145,145, filed Jun. 24,2008, which claims priority from U.S. Provisional Patent Application No.61/091,276, filed Jan. 6, 2008, entitled SYSTEM AND METHOD FOR MESSAGECLUSTERING, which are incorporated herein by reference.

BACKGROUND

A great deal of information is generated when people use electronicdevices, such as when people use mobile phones and cable set-top boxes.Such information, such as location, applications used, social network,physical and online locations visited, to name a few, could be used todeliver useful services and information to end users, and providecommercial opportunities to advertisers and retailers. However, most ofthis information is effectively abandoned due to deficiencies in the waysuch information may be captured. For example, and with respect to amobile phone, information is generally not gathered while the mobilephone is idle (i.e., not being used by a user). Other information, suchas presence of others in the immediate vicinity, time and frequency ofmessages to other users, and activities of a user's social network arealso not captured effectively.

SUMMARY

People often create numerous messages in connection with related topics,events or things, but these messages are often disparate in time, form,communication channel, subject header, or sender/receiver communicationhandles. Using information and relationship processing, as describedfurther herein, embodiments of this disclosure are described thatidentify and pull together messages into useful and meaningful clustersthat are readily available to a user, and actionable by the system andusers, to enhance the management and information extraction potential ofthe myriad messages created and handled by people and processes on adaily basis.

In conventional messaging systems, when a message is sent by a sender,whether an electronic message such as an e-mail, a recorded voicemailmessage, an instant message and other types of electronic messages thatmay be addressed by a sender to one or multiple recipients, the messageis received by each recipient in essentially the same form. In otherwords, the contents of the message are received by the recipient(s) inthe form in which they were sent by the sender. In some instances, knownelectronic mail systems may insert advertising messages or backgroundmessages, which may or may not be under the sender's control, but in anyevent conventional messaging systems heretofore known do not utilizeinformation related to the sender and/or each recipient of the messagein order to individually tailor and create an augmented message, theaugmented message containing information in addition to the informationintended by the sender, and which augmented information is derived bythe network sending the message based upon information known to oravailable to the network about the message as well as the sender and/oreach recipient of the electronic message.

This disclosure describes systems and methods for using data collectedand stored by multiple devices on a network in order to improve theperformance of the services provided via the network. In particular, thedisclosure describes systems and methods delivering communicationsassociated with delivery conditions in which the occurrence of thedelivery condition is determined by monitoring information received froma plurality of sources via multiple communication channels. The messagedelivery systems allow messages to be delivered to any “Who, What, When,Where” from any “Who, What, When, Where” upon the detection of anoccurrence of one or more “Who, What, When, Where” delivery conditions.A message (which may be any data object including text-based messages,audio-based message such as voicemail or other audio such as music orvideo-based prerecorded messages) is delivered in accordance withdelivery conditions based on any available data, including topical,spatial, temporal, and/or social data. Furthermore, because the systemscoordinate delivery of messages via multiple communication channels andthrough multiple devices, the communication channel for delivery of amessage may be dynamically determined based on the delivery conditions.

One aspect of the disclosure is method for delivery messages includingreceiving a plurality of messages for delivery on a network, themessages each containing an identity of at least one recipient. Thereceived messages are parsed to extract message content. Recipient dataavailable to the network is collected by searching for social data,spatial data, temporal data and logical data relating to the messagerecipients and the extracted message content. The messages are analyzedto identify relationships between the extracted message content and themessage recipients. The messages are grouped into a cluster based uponthe identified relationships and a rule set, the cluster comprisingmessages that satisfy rules of the rule set. The cluster is analyzed todetermine a cluster label for identification of the cluster to themessage recipients.

Another aspect of the disclosure is a computer-readable medium encodinginstructions for performing a method for delivery of messages. Themethod includes including receiving a plurality of messages for deliveryon a network, the messages each containing an identity of at least onerecipient. The received messages are parsed to extract message content.Recipient data available to the network is collected by searching forsocial data, spatial data, temporal data and logical data relating tothe message recipients and the extracted message content. The messagesare analyzed to identify relationships between the extracted messagecontent and the message recipients. The messages are grouped into acluster based upon the identified relationships and a rule set, thecluster comprising messages that satisfy rules of the rule set. Thecluster is analyzed to determine a cluster label for identification ofthe cluster to the message recipients.

In yet another aspect, the disclosure describes a system that includes aplurality of processors where an attention engine is implemented on oneof the plurality of processors for receiving a plurality of messages forat least one recipient via a network, wherein the messages contain anidentity of at least one recipient. An attribution engine is implementedon one of the plurality of processors for extracting message contentfrom the received messages. A message intake manager is implemented onone of the plurality of processors for collecting recipient dataavailable to the network by searching for social data, spatial data,temporal data and logical data relating to the at least one recipientand the extracted message content. A correlation engine is implementedon one of the plurality of processors for correlating relationshipsbetween the extracted message content and the at least one recipientassociated with the messages. A cluster building engine is implementedon one of the plurality of processors for forming clusters via groupingthe messages based upon the correlated relationships between theextracted message content and the at least one recipient. Asummarization engine is implemented on one of the plurality ofprocessors for summarizing the content of the messages contained withineach of the clusters formed by the cluster building engine. The systemfurther includes a labeling engine implemented on one of the pluralityof processors for analyzing the summarized cluster content produced bythe summarization engine to determine a label, wherein the labelprovides a visual cluster summarization representation for the at leastone recipient of the clusters.

These and various other features as well as advantages will be apparentfrom a reading of the following detailed description and a review of theassociated drawings. Additional features are set forth in thedescription that follows and, in part, will be apparent from thedescription, or may be learned by practice of the described embodiments.The benefits and features will be realized and attained by the structureparticularly pointed out in the written description and claims hereof aswell as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the disclosure as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawing figures, which form a part of this application,are illustrative of embodiments systems and methods described below andare not meant to limit the scope of the disclosure in any manner, whichscope shall be based on the claims appended hereto.

FIG. 1 illustrates an example of the relationships between RWEs and IOson the W4 COMN.

FIG. 2 illustrates an example of metadata defining the relationshipsbetween RWEs and IOs on the W4 COMN.

FIG. 3 illustrates a conceptual model of the W4 COMN.

FIG. 4 illustrates the functional layers of the W4 COMN architecture.

FIG. 5 illustrates an embodiment of analysis components of a W4 engineas shown in FIG. 2.

FIG. 6 illustrates an embodiment of a W4 engine showing differentcomponents within the sub-engines described generally above withreference to FIG. 5.

FIGS. 7A-C illustrate elements of embodiments of a W4 engine adapted toperform W4 message and cluster delivery as described herein.

FIG. 8 illustrates an embodiment of a method for delivering messagesover a network based on social, temporal, spatial and topical data forentities on the network.

FIG. 9 illustrates a flowchart for an embodiment of augmented messaging.

FIG. 9A illustrates a flowchart for an alternate embodiment.

FIGS. 10A-B illustrate other embodiments of a system for deliveringaugmented messages over a network.

FIG. 11 depicts non-limiting examples of augmented messages sent from asender to three different recipients.

FIG. 12 illustrates a flowchart for an embodiment for creating messageclusters.

FIG. 13 illustrates a flowchart for an embodiment for detection actionsbased on message clusters created, with reference to FIG. 12.

FIGS. 14A-C depict a non-limiting example of message cluster related toan event.

DETAILED DESCRIPTION

This disclosure describes a communication network, referred herein asthe “W4 Communications Network” or W4 COMN, that uses informationrelated to the “Who, What, When and Where” of interactions with thenetwork to provide improved services to the network's users. The W4 COMNis a collection of users, devices and processes that foster bothsynchronous and asynchronous communications between users and theirproxies. It includes an instrumented network of sensors providing datarecognition and collection in real-world environments about any subject,location, user or combination thereof.

As a communication network, the W4 COMN handles the routing/addressing,scheduling, filtering, prioritization, replying, forwarding, storing,deleting, privacy, transacting, triggering of a new message, propagatingchanges, transcoding and linking. Furthermore, these actions can beperformed on any communication channel accessible by the W4 COMN.

The W4 COMN uses a data modeling strategy for creating profiles for notonly users and locations but also any device on the network and any kindof user-defined data with user-specified conditions from a rich set ofpossibilities. Using Social, Spatial, Temporal and Logical dataavailable about a specific user, topic or logical data object, everyentity known to the W4 COMN can be mapped and represented against allother known entities and data objects in order to create both a micrograph for every entity as well as a global graph that interrelates allknown entities against each other and their attributed relations.

In order to describe the operation of the W4 COMN, two elements uponwhich the W4 COMN is built must first be introduced, real-world entitiesand information objects. These distinctions are made in order to enablecorrelations to be made from which relationships betweenelectronic/logical objects and real objects can be determined. Areal-world entity (RWE) refers to a person, device, location, or otherphysical thing known to the W4 COMN. Each RWE known to the W4 COMN isassigned or otherwise provided with a unique W4 identification numberthat absolutely identifies the RWE within the W4 COMN.

RWEs may interact with the network directly or through proxies, whichmay themselves be RWEs. Examples of RWEs that interact directly with theW4 COMN include any device such as a sensor, motor, or other piece ofhardware that connects to the W4 COMN in order to receive or transmitdata or control signals. Because the W4 COMN can be adapted to use anyand all types of data communication, the devices that may be RWEsinclude all devices that can serve as network nodes or generate, requestand/or consume data in a networked environment or that can be controlledvia the network. Such devices include any kind of “dumb” devicepurpose-designed to interact with a network (e.g., cell phones, cabletelevision set top boxes, Fax machines, telephones, and radio frequencyidentification (RFID) tags, sensors, etc.). Typically, such devices areprimarily hardware and their operations can not be considered separatelyfrom the physical device.

Examples of RWEs that must use proxies to interact with W4 COMN networkinclude all non-electronic entities including physical entities, such aspeople, locations (e.g., states, cities, houses, buildings, airports,roads, etc.) and things (e.g., animals, pets, livestock, gardens,physical objects, cars, airplanes, works of art, etc.), and intangibleentities such as business entities, legal entities, groups of people orsports teams. In addition, “smart” devices (e.g., computing devices suchas smart phones, smart set top boxes, smart cars that supportcommunication with other devices or networks, laptop computers, personalcomputers, server computers, satellites, etc.) are also considered RWEsthat must use proxies to interact with the network. Smart devices areelectronic devices that can execute software via an internal processorin order to interact with a network. For smart devices, it is actuallythe executing software application(s) that interact with the W4 COMN andserve as the devices' proxies.

The W4 COMN allows associations between RWEs to be determined andtracked. For example, a given user (an RWE) may be associated with anynumber and type of other RWEs including other people, cell phones, smartcredit cards, personal data assistants, email and other communicationservice accounts, networked computers, smart appliances, set top boxesand receivers for cable television and other media services, and anyother networked device. This association may be made explicitly by theuser, such as when the RWE is installed into the W4 COMN. An example ofthis is the set up of a new cell phone, cable television service oremail account in which a user explicitly identifies an RWE (e.g., theuser's phone for the cell phone service, the user's set top box and/or alocation for cable service, or a username and password for the onlineservice) as being directly associated with the user. This explicitassociation may include the user identifying a specific relationshipbetween the user and the RWE (e.g., this is my device, this is my homeappliance, this person is my friend/father/son/etc., this device isshared between me and other users, etc.). RWEs may also be implicitlyassociated with a user based on a current situation. For example, aweather sensor on the W4 COMN may be implicitly associated with a userbased on information indicating that the user lives or is passing nearthe sensor's location.

An information object (IO), on the other hand, is a logical object thatstores, maintains, generates, serves as a source for or otherwiseprovides data for use by RWEs and/or the W4 COMN. IOs are distinct fromRWEs in that IOs represent data, whereas RWEs may create or consume data(often by creating or consuming IOs) during their interaction with theW4 COMN. Examples of IOs include passive objects such as communicationsignals (e.g., digital and analog telephone signals, streaming media andinterprocess communications), email messages, transaction records,virtual cards, event records (e.g., a data file identifying a time,possibly in combination with one or more RWEs such as users andlocations, that may further be associated with a knowntopic/activity/significance such as a concert, rally, meeting, sportingevent, etc.), recordings of phone calls, calendar entries, web pages,database entries, electronic media objects (e.g., media files containingsongs, videos, pictures, images, audio messages, phone calls, etc.),electronic files and associated metadata.

In addition, IOs include any executing process or application thatconsumes or generates data such as an email communication application(such as OUTLOOK by MICROSOFT, or YAHOO! MAIL by YAHOO!), a calendaringapplication, a word processing application, an image editingapplication, a media player application, a weather monitoringapplication, a browser application and a web page server application.Such active IOs may or may not serve as a proxy for one or more RWEs.For example, voice communication software on a smart phone may serve asthe proxy for both the smart phone and for the owner of the smart phone.

An IO in the W4 COMN may be provided a unique W4 identification numberthat absolutely identifies the IO within the W4 COMN. Although data inan IO may be revised by the act of an RWE, the IO remains a passive,logical data representation or data source and, thus, is not an RWE.

For every IO there are at least three classes of associated RWEs. Thefirst is the RWE who owns or controls the IO, whether as the creator ora rights holder (e.g., an RWE with editing rights or use rights to theIO). The second is the RWE(s) that the IO relates to, for example bycontaining information about the RWE or that identifies the RWE. Thethird are any RWEs who then pay any attention (directly or through aproxy process) to the IO, in which “paying attention” refers toaccessing the IO in order to obtain data from the IO for some purpose.

“Available data” and “W4 data” means data that exists in an IO in someform somewhere or data that can be collected as needed from a known IOor RWE such as a deployed sensor. “Sensor” means any source of W4 dataincluding PCs, phones, portable PCs or other wireless devices, householddevices, cars, appliances, security scanners, video surveillance, RFIDtags in clothes, products and locations, online data or any other sourceof information about a real-world user/topic/thing (RWE) or logic-basedagent/process/topic/thing (IO).

FIG. 1 illustrates an example of the relationships between RWEs and IOson the W4 COMN. In the embodiment illustrated, a user 102 is a RWE ofthe network provided with a unique network ID. The user 102 is a humanthat communicates with the network via the proxy devices 104, 106, 108,110 associated with the user 102, all of which are RWEs of the networkand provided with their own unique network ID. Some of these proxies maycommunicate directly with the W4 COMN or may communicate with the W4COMN via IOs such as applications executed on or by the device.

As mentioned above the proxy devices 104, 106, 108, 110 may beexplicitly associated with the user 102. For example, one device 104 maybe a smart phone connected by a cellular service provider to the networkand another device 106 may be a smart vehicle that is connected to thenetwork. Other devices may be implicitly associated with the user 102.For example, one device 108 may be a “dumb” weather sensor at a locationmatching the current location of the user's cell phone 104, and thusimplicitly associated with the user 102 while the two RWEs 104, 108 areco-located. Another implicitly associated device 110 may be a sensor 110for physical location 112 known to the W4 COMN. The location 112 isknown, either explicitly (through a user-designated relationship, e.g.,this is my home, place of employment, parent, etc.) or implicitly (theuser 102 is often co-located with the RWE 112 as evidenced by data fromthe sensor 110 at that location 112), to be associated with the firstuser 102.

The user 102 may also be directly associated with other people, such asthe person 140 shown, and then indirectly associated with other people142, 144 through their associations as shown. Again, such associationsmay be explicit (e.g., the user 102 may have identified the associatedperson 140 as his/her father, or may have identified the person 140 as amember of the user's social network) or implicit (e.g., they share thesame address).

Tracking the associations between people (and other RWEs as well) allowsthe creation of the concept of “intimacy”: Intimacy being a measure ofthe degree of association between two people or RWEs. For example, eachdegree of removal between RWEs may be considered a lower level ofintimacy, and assigned lower intimacy score. Intimacy may be basedsolely on explicit social data or may be expanded to include all W4 dataincluding spatial data and temporal data.

Each RWE 102, 104, 106, 108, 110, 112, 140, 142, 144 of the W4 COMN maybe associated with one or more IOs as shown. Continuing the examplesdiscussed above, FIG. 1 illustrates two IOs 122, 124 as associated withthe cell phone device 104. One IO 122 may be a passive data object suchas an event record that is used by scheduling/calendaring software onthe cell phone, a contact IO used by an address book application, ahistorical record of a transaction made using the device 104 or a copyof a message sent from the device 104. The other IO 124 may be an activesoftware process or application that serves as the device's proxy to theW4 COMN by transmitting or receiving data via the W4 COMN. Voicecommunication software, scheduling/calendaring software, an address bookapplication or a text messaging application are all examples of IOs thatmay communicate with other IOs and RWEs on the network. The IOs 122, 124may be locally stored on the device 104 or stored remotely on some nodeor datastore accessible to the W4 COMN, such as a message server or cellphone service datacenter. The IO 126 associated with the vehicle 108 maybe an electronic file containing the specifications and/or currentstatus of the vehicle 108, such as make, model, identification number,current location, current speed, current condition, current owner, etc.The IO 128 associated with sensor 108 may identify the current state ofthe subject(s) monitored by the sensor 108, such as current weather orcurrent traffic. The IO 130 associated with the cell phone 110 may beinformation in a database identifying recent calls or the amount ofcharges on the current bill.

Furthermore, those RWEs which can only interact with the W4 COMN throughproxies, such as the people 102, 140, 142, 144, computing devices 104,106 and location 112, may have one or more IOs 132, 134, 146, 148, 150directly associated with them. An example includes IOs 132, 134 thatcontain contact and other RWE-specific information. For example, aperson's IO 132, 146, 148, 150 may be a user profile containing emailaddresses, telephone numbers, physical addresses, user preferences,identification of devices and other RWEs associated with the user,records of the user's past interactions with other RWE's on the W4 COMN(e.g., transaction records, copies of messages, listings of time andlocation combinations recording the user's whereabouts in the past), theunique W4 COMN identifier for the location and/or any relationshipinformation (e.g., explicit user-designations of the user'srelationships with relatives, employers, co-workers, neighbors, serviceproviders, etc.). Another example of a person's IO 132, 146, 148, 150includes remote applications through which a person can communicate withthe W4 COMN such as an account with a web-based email service such asYahoo!Mail. The location's IO 134 may contain information such as theexact coordinates of the location, driving directions to the location, aclassification of the location (residence, place of business, public,non-public, etc.), information about the services or products that canbe obtained at the location, the unique W4 COMN identifier for thelocation, businesses located at the location, photographs of thelocation, etc.

In order to correlate RWEs and IOs to identify relationships, the W4COMN makes extensive use of existing metadata and generates additionalmetadata where necessary. Metadata is loosely defined as data thatdescribes data. For example, given an IO such as a music file, the core,primary or object data of the music file is the actual music data thatis converted by a media player into audio that is heard by the listener.Metadata for the same music file may include data identifying theartist, song, etc., album art, and the format of the music data. Thismetadata may be stored as part of the music file or in one or moredifferent IOs that are associated with the music file or both. Inaddition, W4 metadata for the same music file may include the owner ofthe music file and the rights the owner has in the music file. Asanother example, if the IO is a picture taken by an electronic camera,the picture may include in addition to the primary image data from whichan image may be created on a display, metadata identifying when thepicture was taken, where the camera was when the picture was taken, whatcamera took the picture, who, if anyone, is associated (e.g., designatedas the camera's owner) with the camera, and who and what are thesubjects of/in the picture. The W4 COMN uses all the available metadatain order to identify implicit and explicit associations between entitiesand data objects.

FIG. 2 illustrates an example of metadata defining the relationshipsbetween RWEs and IOs on the W4 COMN. In the embodiment shown, an IO 202includes object data 204 and five discrete items of metadata 206, 208,210, 212, 214. Some items of metadata 208, 210, 212 may containinformation related only to the object data 204 and unrelated to anyother IO or RWE. For example, a creation date, text or an image that isto be associated with the object data 204 of the IO 202.

Some of items of metadata 206, 214, on the other hand, may identifyrelationships between the IO 202 and other RWEs and IOs. As illustrated,the IO 202 is associated by one item of metadata 206 with an RWE 220that RWE 220 is further associated with two IOs 224, 226 and a secondRWE 222 based on some information known to the W4 COMN. This part ofFIG. 2, for example, could describe the relations between a picture (IO202) containing metadata 206 that identifies the electronic camera (thefirst RWE 220) and the user (the second RWE 224) that is known by thesystem to be the owner of the camera 220. Such ownership information maybe determined, for example, from one or another of the IOs 224, 226associated with the camera 220.

FIG. 2 also illustrates metadata 214 that associates the IO 202 withanother IO 230. This IO 230 is itself associated with three other IOs232, 234, 236 that are further associated with different RWEs 242, 244,246. This part of FIG. 2, for example, could describe the relationsbetween a music file (IO 202) containing metadata 206 that identifiesthe digital rights file (the first IO 230) that defines the scope of therights of use associated with this music file 202. The other IOs 232,234, 236 are other music files that are associated with the rights ofuse and which are currently associated with specific owners (RWEs 242,244, 246).

FIG. 3 illustrates a conceptual model of the W4 COMN. The W4 COMN 300creates an instrumented messaging infrastructure in the form of a globallogical network cloud conceptually sub-divided into networked-clouds foreach of the 4Ws: Who, Where, What and When. In the Who cloud 302 are allusers whether acting as senders, receivers, data points orconfirmation/certification sources as well as user proxies in the formsof user-program processes, devices, agents, calendars, etc. In the Wherecloud 304 are all physical locations, events, sensors or other RWEsassociated with a spatial reference point or location. The When cloud306 is composed of natural temporal events (that is events that are notassociated with particular location or person such as days, times,seasons) as well as collective user temporal events (holidays,anniversaries, elections, etc.) and user-defined temporal events(birthdays, smart-timing programs). The What cloud 308 is comprised ofall known data—web or private, commercial or user—accessible to the W4COMN, including for example environmental data like weather and news,RWE-generated data, IOs and IO data, user data, models, processes andapplications. Thus, conceptually, most data is contained in the Whatcloud 308.

As this is just a conceptual model, it should be noted that someentities, sensors or data will naturally exist in multiple clouds eitherdisparate in time or simultaneously. Additionally, some IOs and RWEs maybe composites in that they combine elements from one or more clouds.Such composites may be classified or not as appropriate to facilitatethe determination of associations between RWEs and IOs. For example, anevent consisting of a location and time could be equally classifiedwithin the When cloud 306, the What cloud 308 and/or the Where cloud304.

The W4 engine 310 is center of the W4 COMN's central intelligence formaking all decisions in the W4 COMN. An “engine” as referred to hereinis meant to describe a software, hardware or firmware (or combinationsthereof) system, process or functionality that performs or facilitatesthe processes, features and/or functions described herein (with orwithout human interaction or augmentation). The W4 engine 310 controlsall interactions between each layer of the W4 COMN and is responsiblefor executing any approved user or application objective enabled by W4COMN operations or interoperating applications. In an embodiment, the W4COMN is an open platform upon which anyone can write an application. Tosupport this, it includes standard published APIs for requesting (amongother things) synchronization, disambiguation, user or topic addressing,access rights, prioritization or other value-based ranking, smartscheduling, automation and topical, social, spatial or temporal alerts.

One function of the W4 COMN is to collect data concerning allcommunications and interactions conducted via the W4 COMN, which mayinclude storing copies of IOs and information identifying all RWEs andother information related to the IOs (e.g., who, what, when, whereinformation). Other data collected by the W4 COMN may includeinformation about the status of any given RWE and IO at any given time,such as the location, operational state, monitored conditions (e.g., foran RWE that is a weather sensor, the current weather conditions beingmonitored or for an RWE that is a cell phone, its current location basedon the cellular towers it is in contact with) and current status.

The W4 engine 310 is also responsible for identifying RWEs andrelationships between RWEs and IOs from the data and communicationstreams passing through the W4 COMN. The function of identifying RWEsassociated with or implicated by IOs and actions performed by other RWEsis referred to as entity extraction. Entity extraction includes bothsimple actions, such as identifying the sender and receivers of aparticular IO, and more complicated analyses of the data collected byand/or available to the W4 COMN, for example determining that a messagelisted the time and location of an upcoming event and associating thatevent with the sender and receiver(s) of the message based on thecontext of the message or determining that an RWE is stuck in a trafficjam based on a correlation of the RWE's location with the status of aco-located traffic monitor.

It should be noted that when performing entity extraction from an IO,the IO can be an opaque object with only W4 metadata related to theobject (e.g., date of creation, owner, recipient, transmitting andreceiving RWEs, type of IO, etc.), but no knowledge of the internals ofthe IO (i.e., the actual primary or object data contained within theobject). Knowing the content of the IO does not prevent W4 data aboutthe IO (or RWE) to be gathered. The content of the IO if known can alsobe used in entity extraction, if available, but regardless of the dataavailable entity extraction is performed by the network based on theavailable data. Likewise, W4 data extracted around the object can beused to imply attributes about the object itself, while in otherembodiments, full access to the IO is possible and RWEs can thus also beextracted by analyzing the content of the object, e.g. strings within anemail are extracted and associated as RWEs to for use in determining therelationships between the sender, user, topic or other RWE or IOimpacted by the object or process.

In an embodiment, the W4 engine 310 represents a group of applicationsexecuting on one or more computing devices that are nodes of the W4COMN. For the purposes of this disclosure, a computing device is adevice that includes a processor and memory for storing data andexecuting software (e.g., applications) that perform the functionsdescribed. Computing devices may be provided with operating systems thatallow the execution of software applications in order to manipulatedata.

In the embodiment shown, the W4 engine 310 may be one or a group ofdistributed computing devices, such as a general-purpose personalcomputers (PCs) or purpose built server computers, connected to the W4COMN by suitable communication hardware and/or software. Such computingdevices may be a single device or a group of devices acting together.Computing devices may be provided with any number of program modules anddata files stored in a local or remote mass storage device and localmemory (e.g., RAM) of the computing device. For example, as mentionedabove, a computing device may include an operating system suitable forcontrolling the operation of a networked computer, such as the WINDOWSXP or WINDOWS SERVER operating systems from MICROSOFT CORPORATION.

Some RWEs may also be computing devices such as smart phones,web-enabled appliances, PCs, laptop computers, and personal dataassistants (PDAs). Computing devices may be connected to one or morecommunications networks such as the Internet, a publicly switchedtelephone network, a cellular telephone network, a satellitecommunication network, a wired communication network such as a cabletelevision or private area network. Computing devices may be connectedany such network via a wired data connection or wireless connection suchas a wi-fi, a WiMAX (802.36), a Bluetooth or a cellular telephoneconnection.

Local data structures, including discrete IOs, may be stored on a massstorage device (not shown) that is connected to, or part of, any of thecomputing devices described herein including the W4 engine 310. Forexample, in an embodiment, the data backbone of the W4 COMN, discussedbelow, includes multiple mass storage devices that maintain the IOs,metadata and data necessary to determine relationships between RWEs andIOs as described herein. A mass storage device includes some form ofcomputer-readable media and provides non-volatile storage of data andsoftware for retrieval and later use by one or more computing devices.Although the description of computer-readable media contained hereinrefers to a mass storage device, such as a hard disk or CD-ROM drive, itshould be appreciated by those skilled in the art that computer-readablemedia can be any available media that can be accessed by a computingdevice.

By way of example, and not limitation, computer-readable media maycomprise computer storage media and communication media. Computerstorage media include volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solidstate memory technology, CD-ROM, DVD, or other optical storage, magneticcassette, magnetic tape, magnetic disk storage or other magnetic storagedevices, or any other medium which can be used to store the desiredinformation and which can be accessed by the computer.

FIG. 4 illustrates the functional layers of the W4 COMN architecture. Atthe lowest layer, referred to as the sensor layer 402, is the network404 of the actual devices, users, nodes and other RWEs. Theinstrumentation of the network nodes to utilize them as sensors includeknown technologies like web analytics, GPS, cell-tower pings, use logs,credit card transactions, online purchases, explicit user profiles andimplicit user profiling achieved through behavioral targeting, searchanalysis and other analytics models used to optimize specific networkapplications or functions.

The next layer is the data layer 406 in which the data produced by thesensor layer 402 is stored and cataloged. The data may be managed byeither the network 404 of sensors or the network infrastructure 406 thatis built on top of the instrumented network of users, devices, agents,locations, processes and sensors. The network infrastructure 408 is thecore under-the-covers network infrastructure that includes the hardwareand software necessary to receive that transmit data from the sensors,devices, etc. of the network 404. It further includes the processing andstorage capability necessary to meaningfully categorize and track thedata created by the network 404.

The next layer of the W4 COMN is the user profiling layer 410. Thislayer 410 may further be distributed between the network infrastructure408 and user applications/processes 412 executing on the W4 engine ordisparate user computing devices. In the user profiling layer 410 thatfunctions as W4 COMN's user profiling layer 410. Personalization isenabled across any single or combination of communication channels andmodes including email, IM, texting (SMS, etc.), photobloging, audio(e.g. telephone call), video (teleconferencing, live broadcast), games,data confidence processes, security, certification or any other W4 COMNprocess call for available data.

In one embodiment, the user profiling layer 410 is a logic-based layerabove all sensors to which sensor data are sent in the rawest form to bemapped and placed into the W4 COMN data backbone 420. The data(collected and refined, related and deduplicated, synchronized anddisambiguated) are then stored in one or a collection of relateddatabases available to all processes of all applications approved on theW4 COMN. All Network-originating actions and communications are basedupon the fields of the data backbone, and some of these actions are suchthat they themselves become records somewhere in the backbone, e.g.invoicing, while others, e.g. fraud detection, synchronization,disambiguation, can be done without an impact to profiles and modelswithin the backbone.

Actions originating from anything other than the network, e.g., RWEssuch as users, locations, proxies and processes, come from theapplications layer 414 of the W4 COMN. Some applications may bedeveloped by the W4 COMN operator and appear to be implemented as partof the communications infrastructure 408, e.g. email or calendarprograms because of how closely the operate with the sensor processingand user profiling layer 410. The applications 412 also serve some roleas a sensor in that they, through their actions, generate data back tothe data layer 406 via the data backbone concerning any data created oravailable due to the applications execution.

The applications layer 414 also provides a personalized user interface(UI) based upon device, network, carrier as well as user-selected orsecurity-based customizations. Any UI can operate within the W4 COMN ifit is instrumented to provide data on user interactions or actions backto the network. This is a basic sensor function of any W4 COMNapplication/UI, and although the W4 COMN can interoperate withapplications/UIs that are not instrumented, it is only in a deliverycapacity and those applications/UIs would not be able to provide anydata (let alone the rich data otherwise available from W4-enableddevices.)

In the case of W4 COMN mobile devices, the UI can also be used toconfirm or disambiguate incomplete W4 data in real-time, as well ascorrelation, triangulation and synchronization sensors for other nearbyenabled or non-enabled devices. At some point, the network effects ofenough enabled devices allow the network to gather complete or nearlycomplete data (sufficient for profiling and tracking) of a non-enableddevice because of it's regular intersection and sensing by enableddevices in it's real-world location.

Above the applications layer 414 (and sometimes hosted within it) is thecommunications delivery network(s) 416. This can be operated by the W4COMN operator or be independent third-party carrier service, but ineither case it functions to deliver the data via synchronous orasynchronous communication. In every case, the communication deliverynetwork 414 will be sending or receiving data (e.g., http or IP packets)on behalf of a specific application or network infrastructure 408request.

The communication delivery layer 418 also has elements that act assensors including W4 entity extraction from telephone calls, emails,blogs, etc. as well as specific user commands within the deliverynetwork context, e.g., “save and prioritize this call” said before endof call may trigger a recording of the previous conversation to be savedand for the W4 entities within the conversation to analyzed andincreased in weighting prioritization decisions in thepersonalization/user profiling layer 410.

FIG. 5 illustrates an embodiment of analysis components of a W4 engineas shown in FIG. 3. As discussed above, the W4 Engine is responsible foridentifying RWEs and relationships between RWEs and IOs from the dataand communication streams passing through the W4 COMN.

In one embodiment the W4 engine connects, interoperates and instrumentsall network participants through a series of sub-engines that performdifferent operations in the entity extraction process. One suchsub-engine is an attribution engine 504. The attribution engine 504tracks the real-world ownership, control, publishing or otherconditional rights of any RWE in any IO. Whenever a new IO is detectedby the W4 engine 502, e.g., through creation or transmission of a newmessage, a new transaction record, a new image file, etc., ownership isassigned to the IO. The attribution engine 504 creates this ownershipinformation and further allows this information to be determined foreach IO known to the W4 COMN.

The W4 engine 502 further includes a correlation engine 506. Thecorrelation engine 506 operates in two capacities: first, to identifyassociated RWEs and IOs and their relationships (such as by creating acombined graph of any combination of RWEs and IOs and their attributes,relationships and reputations within contexts or situations) and second,as a sensor analytics pre-processor for attention events from anyinternal or external source.

In one embodiment, the identification of associated RWEs and IOsfunction of the correlation engine 506 is done by graphing the availabledata. In this embodiment, a histogram of all RWEs and IOs is created,from which correlations based on the graph may be made. Graphing, or theact of creating a histogram, is a computer science method of identify adistribution of data in order to identify relevant information and makecorrelations between the data. In a more general mathematical sense, ahistogram is simply a mapping m_(i) that counts the number ofobservations that fall into various disjoint categories (known as bins),whereas the graph of a histogram is merely one way to represent ahistogram. By selecting each IO, RWE, and other known parameters (e.g.,times, dates, locations, etc.) as different bins and mapping theavailable data, relationships between RWEs, IOs and the other parameterscan be identified.

In an embodiment, the W4 data are processed and analyzed using datamodels that treat data not as abstract signals stored in databases, butrather as IOs that represent RWEs that actually exist, have existed, orwill exist in real space, real time, and are real people, objects,places, times, and/or events. As such, the data model for W4 IOs thatrepresent W4 RWEs (Where/When/Who/What) will model not only the signalsrecorded from the RWEs or about the RWEs, but also represent these RWEsand their interactions in ways that model the affordances andconstraints of entities and activities in the physical world. A notableaspect is the modeling of data about RWEs as embodied and situated inreal world contexts so that the computation of similarity, clustering,distance, and inference take into account the states and actions of RWEsin the real world and the contexts and patterns of these states andactions.

For example, for temporal data the computation of temporal distance andsimilarity in a W4 data model cannot merely treat time as a linearfunction. The temporal distance and similarity between two times isdependent not only on the absolute linear temporal delta between them(e.g., the number of hours between “Tuesday, November 20, 4:00 pmPacific Time” and “Tuesday, November 20, 7:00 pm Pacific Time”), buteven more so is dependent on the context and activities that conditionthe significance of these times in the physical world and the other W4RWEs (people, places, objects, and events) etc.) associated with them.For example, in terms of distance and similarity, “Tuesday, November 20,4:00 pm Pacific Time” and “Tuesday, November 27, 4:00 pm Pacific Time”may be modeled as closer together in a W4 temporal data model than“Tuesday, November 20, 4:00 pm Pacific Time” and “Tuesday, November 20,7:00 pm Pacific Time” because of the weekly meeting that happens everyTuesday at work at 4:00 pm vs. the dinner at home with family thathappens at 7 pm on Tuesdays. Contextual and periodic patterns in timemay be important to the modeling of temporal data in a W4 data model.

An even simpler temporal data modeling issue is to model the variousperiodic patterns of daily life such as day and night (and subperiodswithin them such as morning, noon, afternoon, evening, etc.) and thedistinction between the workweek and the weekend. In addition, salientperiods such as seasons of the year and salient events such as holidaysalso affect the modeling of temporal data to determine similarity anddistance. Furthermore, the modeling of temporal data for IOs thatrepresent RWEs should correlate temporal, spatial, and weather data toaccount for the physical condition of times at different points on theplanet. Different latitudes have different amounts of daylight and evenare opposite between the northern and southern hemispheres. Similarcontextual and structural data modeling issues arise in modeling datafrom and about the RWEs for people, groups of people, objects, places,and events.

With appropriate data models for IOs that represent data from or aboutRWEs, a variety of machine learning techniques can be applied to analyzethe W4 data. In an embodiment, W4 data may modeled as a “feature vector”in which the vector includes not only raw sensed data from or about W4RWEs, but also higher order features that account for the contextual andperiodic patterns of the states and action of W4 RWEs. Each of thesefeatures in the feature vector may have a numeric or symbolic value thatcan be compared for similarity to other numeric or symbolic values in afeature space. Each feature may also be modeled with an additional valuefrom 0 to 1 (a certainty value) to represent the probability that thefeature is true. By modeling W4 data about RWEs in ways that account forthe affordances and constraints of their context and patterns in thephysical world in features and higher order features with or withoutcertainty values, this data (whether represented in feature vectors orby other data modeling techniques) can then be processed to determinesimilarity, difference, clustering, hierarchical and graphrelationships, as well as inferential relationships among the featuresand feature vectors.

A wide variety of statistical and machine learning techniques can beapplied to W4 data from simple histograms to Sparse Factor Analysis(SFA), Hidden Markov Models (HMMs), Support Vector Machines (SVMs),Bayesian Methods, etc. Such learning algorithms may be populated withdata models that contain features and higher order features representnot just the “content” of the signals stored as IOs, e.g., the raw W4data, but also model the contexts and patterns of the RWEs that exist,have existed, or will exist in the physical world from which these datahave been captured.

As a pre-processor, the correlation engine 506 monitors the informationprovided by RWEs in order to determine if any conditions are identifiedthat may trigger an action on the part of the W4 engine 502. Forexample, if a delivery condition has be associated with a message, whenthe correlation engine 506 determines that the condition is met, it cantransmit the appropriate trigger information to the W4 engine 502 thattriggers delivery of the message.

The attention engine 508 instruments all appropriate network nodes,clouds, users, applications or any combination thereof and includesclose interaction with both the correlation engine 506 and theattribution engine 504.

FIG. 6 illustrates an embodiment of a W4 engine showing differentcomponents within the sub-engines described generally above withreference to FIG. 4. In one embodiment the W4 engine 600 includes anattention engine 608, attribution engine 604 and correlation engine 606with several sub-managers based upon basic function.

The attention engine 608 includes a message intake and generationmanager 610 as well as a message delivery manager 612 that work closelywith both a message matching manager 614 and a real-time communicationsmanager 616 to deliver and instrument all communications across the W4COMN.

The attribution engine 604 works within the user profile manager 618 andin conjunction with all other modules to identify, process/verify andrepresent ownership and rights information related to RWEs, IOs andcombinations thereof.

The correlation engine 606 dumps data from both of its channels (sensorsand processes) into the same data backbone 620 which is organized andcontrolled by the W4 analytics manager 622 and includes both aggregatedand individualized archived versions of data from all network operationsincluding user logs 624, attention rank place logs 626, web indices andenvironmental logs 618, e-commerce and financial transaction information630, search indexes and logs 632, sponsor content or conditionals, adcopy and any and all other data used in any W4 COMN process, IO orevent. Because of the amount of data that the W4 COMN will potentiallystore, the data backbone 620 includes numerous database servers anddatastores in communication with the W4 COMN to provide sufficientstorage capacity.

As discussed above, the data collected by the W4 COMN includes spatialdata, temporal data, RWE interaction data, IO content data (e.g., mediadata), and user data including explicitly-provided and deduced socialand relationship data. Spatial data may be any data identifying alocation associated with an RWE. For example, the spatial data mayinclude any passively collected location data, such as cell tower data,global packet radio service (GPRS) data, global positioning service(GPS) data, WI-FI data, personal area network data, IP address data anddata from other network access points, or actively collected locationdata, such as location data entered by the user.

Temporal data is time based data (e.g., time stamps) that relate tospecific times and/or events associated with a user and/or theelectronic device. For example, the temporal data may be passivelycollected time data (e.g., time data from a clock resident on theelectronic device, or time data from a network clock), or the temporaldata may be actively collected time data, such as time data entered bythe user of the electronic device (e.g., a user maintained calendar).

The interaction data may be any data associated with user interaction ofthe electronic device, whether active or passive. Examples ofinteraction data include interpersonal communication data, media data,relationship data, transactional data and device interaction data, allof which are described in further detail below. Table 1, below, is anon-exhaustive list including examples of electronic data.

TABLE 1 Examples of Electronic Data Spatial Data Temporal DataInteraction Data Cell tower data Time stamps Interpersonal GPRS dataLocal clock communication data GPS data Network clock Media data WiFidata User input of Relationship data Personal area network data timedata Transactional data Network access points data Device interactiondata User input of location data Geo-coordinates data

With respect to the interaction data, communications between any RWEsmay generate communication data that is transferred via the W4 COMN. Forexample, the communication data may be any data associated with anincoming or outgoing short message service (SMS) message, email message,voice call (e.g., a cell phone call, a voice over IP call), or othertype of interpersonal communication relative to an RWE, such asinformation regarding who is sending and receiving the communication(s).As described above, communication data may be correlated with, forexample, temporal data to deduce information regarding frequency ofcommunications, including concentrated communication patterns, which mayindicate user activity information.

Logical and IO data refers to the data contained by an IO as well asdata associated with the IO such as creation time, owner, associatedRWEs, when the IO was last accessed, etc. If the IO is a media object,the term media data may be used. Media data may include any datarelating to presentable media, such as audio data, visual data, andaudiovisual data. For example, the audio data may be data relating todownloaded music, such as genre, artist, album and the like, andincludes data regarding ringtones, ringbacks, media purchased,playlists, and media shared, to name a few. The visual data may be datarelating to images and/or text received by the electronic device (e.g.,via the Internet or other network). The visual data may be data relatingto images and/or text sent from and/or captured at the electronicdevice. The audiovisual data may be data associated with any videoscaptured at, downloaded to, or otherwise associated with the electronicdevice. The media data includes media presented to the user via anetwork, such as use of the Internet, and includes data relating to textentered and/or received by the user using the network (e.g., searchterms), and interaction with the network media, such as click data(e.g., advertisement banner clicks, bookmarks, click patterns and thelike). Thus, the media data may include data relating to the user's RSSfeeds, subscriptions, group memberships, game services, alerts, and thelike. The media data also includes non-network activity, such as imagecapture and/or video capture using an electronic device, such as amobile phone. The image data may include metadata added by the user, orother data associated with the image, such as, with respect to photos,location when the photos were taken, direction of the shot, content ofthe shot, and time of day, to name a few. As described in further detailbelow, media data may be used, for example, to deduce activitiesinformation or preferences information, such as cultural and/or buyingpreferences information.

The relationship data may include data relating to the relationships ofan RWE or IO to another RWE or IO. For example, the relationship datamay include user identity data, such as gender, age, race, name, socialsecurity number, photographs and other information associated with theuser's identity. User identity information may also include e-mailaddresses, login names and passwords. Relationship data may furtherinclude data identifying explicitly associated RWEs. For example,relationship data for a cell phone may indicate the user that owns thecell phone and the company that provides the service to the phone. Asanother example, relationship data for a smart car may identify theowner, a credit card associated with the owner for payment of electronictolls, those users permitted to drive the car and the service stationfor the car.

Relationship data may also include social network data. Social networkdata includes data relating to any relationship that is explicitlydefined by a user or other RWE, such as data relating to a user'sfriends, family, co-workers, business relations, and the like. Socialnetwork data may include, for example, data corresponding with auser-maintained electronic address book. Relationship data may becorrelated with, for example, location data to deduce social networkinformation, such as primary relationships (e.g., user-spouse,user-children and user-parent relationships) or other relationships(e.g., user-friends, user-co-worker, user-business associaterelationships). Relationship data also may be utilized to deduce, forexample, activities information.

The interaction data may also include transactional data. Thetransactional data may be any data associated with commercialtransactions undertaken by or at the mobile electronic device, such asvendor information, financial institution information (e.g., bankinformation), financial account information (e.g., credit cardinformation), merchandise information and costs/prices information, andpurchase frequency information, to name a few. The transactional datamay be utilized, for example, to deduce activities and preferencesinformation. The transactional information may also be used to deducetypes of devices and/or services the user owns and/or in which the usermay have an interest.

The interaction data may also include device or other RWE interactiondata. Such data includes both data generated by interactions between auser and a RWE on the W4 COMN and interactions between the RWE and theW4 COMN. RWE interaction data may be any data relating to an RWE'sinteraction with the electronic device not included in any of the abovecategories, such as habitual patterns associated with use of anelectronic device data of other modules/applications, such as dataregarding which applications are used on an electronic device and howoften and when those applications are used. As described in furtherdetail below, device interaction data may be correlated with other datato deduce information regarding user activities and patterns associatedtherewith. Table 2, below, is a non-exhaustive list including examplesof interaction data.

TABLE 2 Examples of Interaction Data Type of Data Example(s)Interpersonal Text-based communications, such as SMS and e-mailcommunication Audio-based communications, such as voice calls, voicedata notes, voice mail Media-based communications, such as multimediamessaging service (MMS) communications Unique identifiers associatedwith a communication, such as phone numbers, e-mail addresses, andnetwork addresses Media data Audio data, such as music data (artist,genre, track, album, etc.) Visual data, such as any text, images andvideo data, including Internet data, picture data, podcast data andplaylist data Network interaction data, such as click patterns andchannel viewing patterns Relationship User identifying information, suchas name, age, gender, data race, and social security number Socialnetwork data Transactional Vendors data Financial accounts, such ascredit cards and banks data Type of merchandise/services purchased Costof purchases Inventory of purchases Device Any data not captured abovedealing with user interaction interaction of the device, such aspatterns of use of the device, data applications utilized, and so forth

Augmented Delivery of Messages on the W4 COMN or Other Networks

One notable aspect of the W4 COMN is the ability to use W4 data to allowusers to tailor when and how messages are delivered to other users ortheir proxies. The information obtained about a W4 entity from anysource or communication channel may be used as a basis for deliveryconditions for any message delivered via the W4 COMN on anycommunication channel interoperating with the W4 COMN.

The delivery of messages is a network personal information management(PIM) operation that allows both explicit and implicit automation of W4COMN circuits, processes and events by logic-based conditions includingmeans for senders, receivers and delivery conditions to be expressed,weighted and prioritized in W4 analytical processes for testing deliveryconditions or network environmental conditions. W4 message deliveryincludes user-, process- or system-generated messages targeted to anintersection of any topical, spatial, temporal, and/or social variables.

To continue the “Who, What, When, Where” conceptualization discussedabove, W4 message delivery allows messages to be delivered to any “Who,What, When, Where” from any “Who, What, When, Where” upon the detectionof an occurrence of one or more “Who, What, When, Where” deliveryconditions. Table 3, below, provides a matrix of some examples ofdifferent “Who, What, When, Where” combinations that could be used in W4message delivery. The listings in Table 3 are not complete norexhaustive, but are provided to give an idea of the plethora ofdifferent message delivery options provided by the W4 COMN.

TABLE 3 Examples of W4 Message Delivery Delivery From Conditions ToExplanation Example Message Who When Who Person to Person based on timeHappy Birthday! (standard phone or email) or future scheduled (send todaughter on birthday) Who Who Who Person to Person based on Social StayAway from that Conditions (messages between Boy.; Don't talk to peoplebased on whether other strangers. people are present or not or detectionof a social relationship) Who Where Who Person to Person based on Don'tforget to buy location (send shopping list when milk. roommate entersstore) Who What Who Person to Person based on This movie is too scarystate/topic (raining reminder, for you.; It will freeze content monitor,surf conditions, tonight - drain the stock monitor) sprinklers. WhereWhen Who Location to Person based on time Don't forget your wife's(special lunch deals now, birthday at Macy's. birthday alert reminder,costa rica invites you to warmer weather, mardi gras) Where Who WhoLocation to Person based on Bring your friend back Person (new adformat, lovers to Jamaica. enjoy Italian food at Joe's) Where Where WhoLocation to Person based Don't buy Starbucks, Location(comparative/poaching come to coffee people.; advertising, sub-areaspecialized Welcome to Boston eat calls at conference) here. Where WhatWho Location to Person based on Umbrellas available in State/Topic(personalized offers the lobby. from venue based on profile,personalized coupons) When When Who Time to Person based on time It'ssummertime don't (calendar updates user, seasonal forget your sunblock.;x reminders) shopping days to Christmas When Who Who Time to Personbased on Person Ask Sally what she (holiday messages to spouse wants foryour when partner is near) anniversary. When Where Who Time to Personbased on Don't forget to come to Location (event messages patron; thePride Parade.; It's Macy's is closing at 9:00 pm; Happy Hour at Joe's.rush hour re-route) When What Who Time to Person based on Stop readingemail and State/Topic (can be based on a play with your kids. state of avariable such as rain sensor or on the output of a process) What WhenWho State/Topic to Person based on Car says change your Time(user-defined or process- oil.; Fridge wants more generated message)beer.; Your hair wants to be cut. What Who Who State/Topic to Personbased on Diamonds are forever Person delivered when you meet wife fordinner.; Better safe than sorry, use mouthwash. What Where WhoState/Topic to Person based on Don't' drink and drive.; Location (publicservice Don't' forget to buy announcement) cheese.; Constructionscheduled for next week, use alternate route. What What Who State/Topicto Person based on Your dreams want you State/Topic (medicaladvertising) back.; Losing your hair. Who When Where Person to Locationbased on Happy New Year.; Call Time a cab as bar closes.; Did Suzie showup for ballet class? Who Who Where Person to Location based on Don't letSam drink.; Person (restraining order Jane is authorized to enforcement,messages to pick up my child.; This businesses, schools, etc.) patron isnot of legal age. Who Where Where Person to Location based on Make me areservation Location at Joe's when I arrive in NYC.; Get my room ready.Who What Where Person to Location based on Diabetic alert to State/Topichospital.; Save me the nightly special.; Stop Suzie from using librarycomputer to look at porn. Where When Where Location to Location based onFlight 85 is delayed.; Time Kids on the way walking home (earlydismissal). Where Who Where Location to Location based on Elite customeris being Person sent over, please treat well.; Suzie has arrived safelyat after school. Where Where Where Location to Location based onConference room Location contacts lobby because meeting is in newlocation. Where What Where Location to Location based on Airport updatesthe State/Topic (automatic inventory hotel that I'm stuck in restockorder) traffic in a cab.; Full hotel looks for rooms at close by. WhenWhen Where Time to Location based on Weekly meeting Time contactsconference room of time change.; Thanksgiving reminds Safeway only twodays to holiday. When Who Where Time to Location based on Lent sendsmessage to Person restaurant to only serve fish.; 4:00 pm contactsschool to confirm arrival.; Elvis the left the building. When WhereWhere Time to Location based on Weekly meeting Location contactsconference room because attendees are in Japan When What Where Time toLocation based on Wedding to reception State/Topic hall that itsraining/xtra attendees. What When Where State/Topic to Location based onCar to vendor(s) for oil Time change. What Who Where State/Topic toLocation based on Diet to restaurant for Person special mealrequirements. What Where Where State/Topic to Location based onEducation plan to Location bookstore when child enters the mall.; Car totow truck venue(s) with car location. What What Where State/Topic toLocation based on Refrigerator to Safeway State/Topic for more eggs. WhoWhen When Person to Time based on Time One week before my birthday,broadcast.; schedule reminder for annual physical. Who Who When Personto Time based on Person Get STD test next week after dating that person.Who Where When Person to Time based on Schedule dinner with LocationMarc when he is in town. Who What When Person to Time based on Send amessage to the State/Topic future to renew warranty. Where When WhenPlace to Time based on Time Gallery sends reminders based upon a openingnight, Macy's sends out “One week to the Men's sale.” Where Who WhenPlace to Time based on Person Cody's books sends to calendar that AlGore is speaking. Where Where When Place to Time based on LocationRestaurant sends out a message in a given radius “we are closing in anhour and still have tables.” Where What When Place to Time based onVendor contacts loyal State/Topic customer after six weeks of no show.When When When Time to Time based on Time Calendar to calendar based onmissed meeting. When Who When Time to Time based on Person Calendar tocalendar based on required attendee's absence. When Where When Time toTime based on Location Calendar to Calendar based on room availability.When What When Time to Time based on Calendar to Calendar State/Topicbased on subject matter of meeting.; AC filter replacement. What WhenWhen State/Topic to Time based on AC to calendar based on Time servicecycle.; Diet plan to calendar to schedule exercise due to lack thereof.What Who When State/Topic to Time based on Event Plan to lunchtimePerson for vegetarian based on Marc joining.; collaborative restaurantrecommendation. What Where When State/Topic to Time based on Put yourchains on Location (reminders to buy things before going over based onwhen/where you are; Grant's pass; product to calendar with Strawberriesshould be availability for locations) ripe next week. What What WhenState/Topic to Time based on Refrigerator to Sunday State/Topic thatmilk is expired.; Car to weekend-wash. Who When When Person to Timebased on Time Schedule annual/recurring maintenance.; Who Who WhenPerson to Time based on Person Auto birthday reminder based on socialinteraction.; Parent schedules discussion with kid based onassociations. Who Where When Person to Time based on Make follow upLocation (travel reminder to leave appointments.; Wake up in time toreach destination on message to spouse when time; auto refresh oncalendar to traveling.; Closing push out rest of day by x time) noticetriggered by proximity. Who What When Person to Time based on Santa toChristmas that State/Topic (schedule future Johnny is Bad.; cancel eventbased on past due now; dinner because I'm schedule discussion on onlinesick.; schedule gym browsing with child based on surf time because I atetoo history) much.; Where When When Place to Time based on Time Meetingroom to calendar that current meeting is going long.; School to calendarthat students are getting out early. Where Who When Place to Time basedon Person Venue to calendars that (Tuesday kids eat free; Wed iscelebrity ate here; theme ladies night) park to calendar for summer campbased on visit with child Where Where When Place to Time based onLocation SFO to calendar that plane is still in Denver; School tocalendar based on museum visit. Where What When Place to Time based onVenue to calendar to State/Topic leave early due to traffic/terroristthreat.; farm to market calendar of expected harvest When When What Timeto State/Topic based on Calendar to Car based Time on 3 months sincelast oil change.; calendar to alarm clock/coffee maker. When Who WhatTime to State/Topic based on Calendar to Refrigerator Person based onUncle Joe staying with us. When Where What Time to State/Topic based onCalendar to bathtub Location based on being at gym. When What What Timeto State/Topic based on Birthday to car based on State/Topic webbrowsing.; sunset to car to make sure the door's are locked. What WhenWhat State/Topic to State/Topic based Alarm clock to washing on Timemachine/coffee machine with start time. What Who What State/Topic toState/Topic based Car to clothes because on Person of association.;Dating plan to clothes not to be worn around sally.; Dating plan totoothbrush to be used and brought. What Where What State/Topic toState/Topic based 12 steps to car based on on Location entering bar.;Weight to belt based on restaurant.; Badge beeps car that it has beenforgotten. What What What State/Topic to State/Topic based Shirt towashing on State/Topic machine to remove black sock from whites.; Forgotitem to backpack. Who When What Person to State/Topic based onProgramming alarm Time clock.; Attorney registering with barassociation.; Tell the bath to start at 6:00 pm Who Who What Person toState/Topic based on Parent to child's Person clothes.; Adaptive fashionbased on association.; Bumper stickers on car change based on who iswatching. Who Where What Person to State/Topic based on Customizedcontent Location (user to user-defined instructions depending plan basedon attention) on locale.; Auto- changing location sensitive ringtones.Who What What Person to State/Topic based on Wife to marriage planState/Topic (user to user-defined based on husband's plan based onfeedback; parent to surfing.; Message to car phone if drugs are present-to beep when less than behavior modification space) half gas.; WhereWhen What Place to State/Topic based on Dealership to car to Time comein for tune up. Where Who What Place to State/Topic based on Restaurantto car based Person on occupants.; Playground to parenting plan based onplay. Where Where What Place to State/Topic based on Parking lot to carwith Location closest vacancies/best prices. Where What What Place toState/Topic based on Repair vendor to car State/Topic after accident.

The list provided in Table 3 is a very limited list of the possibilitiesfor message delivery via the W4 COMN. It should be noted that thedelivery conditions could be a simple condition, such as a time ordetection that a designated RWE is at a location, or a more complexcondition based on the occurrence of multiple conditions, either at thesame time or in a specific order, such as deliver only on the day of abaseball game to a RWE near a specified location. In the baseball gameexample, the baseball game may be considered to be an event, with itsown unique W4 identifier, that is associated with a location and a timeperiod. For events such as sporting events, meetings, holidays, etc.,one or more IOs may exist on the W4 COMN or an electronic calendar thatare a proxy for the event from which the time, location, and otherrelevant data of the event may be obtained.

In a broad sense, W4 message delivery allows a message (which may be anyIO including text-based messages, audio-based message such as voicemailor other audio such as music or video-based prerecorded messages) to bedelivered in accordance with delivery conditions based on anycombination of the available W4 data types, including topical, spatial,temporal, and/or social data. Furthermore, because the W4 COMNcoordinates delivery of messages via multiple communication channels andthrough multiple devices and other RWEs, it allows the communicationchannel for delivery of a message to be dynamically determined upondetection that the delivery conditions are met. Examples include asocial alarm clock, place-based messages, social proximity-basedmessages, and time-shifted message delivery, to name but a fewapplications of the W4 message delivery functionality.

Predetermined sets of W4 delivery conditions can be packaged andprovided to users in common bundles, e.g., a Parent's Package, a Boss'Package, a Vehicle Maintenance Package, etc. These bundles may includepredetermined message content, delivery conditions and deliverycondition templates that allow the users to quickly construct deliveryconditions for messages that will be easily and clearly interpreted bythe W4 COMN's message delivery subsystems.

FIG. 7A illustrates elements of an embodiment of a W4 engine adapted toperform W4 enhanced message delivery as described herein. The W4 engine700 includes a correlation engine 506, an attribution engine 504 and anattention engine 508 as described above. The W4 engine 700 is providedwith a message intake manager 702 that is adapted to receive messagesand their associated delivery conditions from senders via the variouscommunication channels interoperating with the W4 COMN. The W4 engine700 further includes a message delivery manager 704, an identificationengine 706 and a message enhancement manager 708.

As discussed above, it should be understood that multiple RWEs and IOsmay be associated with a single message as a sender. For example, a usermay create and send an email message with delivery conditions using alaptop computer. The user is an RWE having a unique W4 identifier. Inaddition, the laptop is an RWE with its own unique W4 identifier. Theemail application on the laptop may be tracked as an IO with its own W4identifier. In an embodiment, some or all of the user, laptop computerand email application may be considered a sender of the IO that is theemail message. In this case, the user may be considered the originatingsender and the laptop and email application proxies for the originatingsender. The concept of proxies was discussed above and is particularlyimportant here where it is anticipated that human actors, either assenders, recipients or entities tied to a delivery condition, will beknown to the W4 COMN primarily through information obtained from theirproxies (e.g., proxy RWEs, such as their smart phones, computingdevices, sensors, smart vehicles, home phones, etc., and proxy IOs suchas email accounts, communication software, credit card accounts, dataobjects containing data generated by a RWE, data objects containing dataabout an RWE or event, etc.).

In an embodiment, this determination of the senders of a message,including determining who the user, if any, is that should be consideredthe original sender may be performed by the attribution engine 504 asdescribed above. It should also be noted that some messages may be sentby a process programmatically, e.g., automatically during the course ofthe execution of a program, so that there is not a human sender to beidentified but rather only a sender IO. Furthermore, the attributionengine 504 parses the received message and identifies W4 data for RWEsand IOs associated with the message. Alternatively, the attributionengine 504 may only identify the sending RWE that actually places themessage into the W4 COMN and any other associated RWEs (e.g., proxiesand/or originating sender) may be identified by the message intakemanager 702 in conjunction with the correlation engine 506.

The W4 engine 700 also includes an identification engine 706 that isadapted to identify the message type and match the content output fromthe correlation engine 506 to the message IO. The identification engine706 analyzes a series of possible augmentations in view of W4 entitieswithin each message type.

The message intake manager 702, upon receipt of a message with deliveryconditions, identifies the recipients and the delivery conditions of themessage as described below. This may include requesting that thecorrelation engine 506 correlate the channel-specific identifiers of therecipients with other W4 data in order to identify a target humanrecipient, if any, and any proxies for that recipient. In addition, thesame information may need to be determined for RWEs identified in thedelivery conditions.

It should be understood that any human or non-networked entity that is asender, recipient or subject of a delivery condition of a message may beidentified only by proxy RWEs or IOs. For example, an email may be sentby or directed to “john.smith@yahoo.com” or a telephone call may bedirected to “(720)555-0505.” In both cases, the identifiers used toidentify the human (i.e., “john.smith@yahoo.com” and “(720)555-0505”)are identifiers of proxies of the actual intended human recipient. Basedon the W4 data known to the W4 COMN, these identifiers of proxies may beanalyzed, e.g., by the correlation engine 506, in order to determine theunique W4 identifier of the RWE that is accessed by, represented by orworking through the proxy RWE or proxy IO. For example,“john.smith@yahoo.com” and “(720)555-0505” may be communicationchannel-specific identifiers of RWEs or IOs that the W4 COMN is awareare proxies for a known human RWE (e.g., a user with the name JohnSmith) with a distinct unique W4 identifier.

A message enhancement manager 708 combines the matched content and themessage IO output from the identification engine 706 for delivery to therecipient. The W4 engine 700 further includes the message deliverymanager 704 that controls the delivery of messages. In an embodiment,the message delivery manager 704 logs the delivery conditions for amessage and monitors the W4 data for occurrence of the deliveryconditions. When/if the delivery conditions are met, the messagedelivery manager 704 then delivers the message to the recipient. Thismay include selecting a delivery route or communication channel andselecting the appropriate proxy RWE (if applicable) for delivery of themessage, possibly including reformatting the message for the selectedRWE. For example, for an email message that is to be delivered to arecipient when that recipient is at a specified location, the emailmessage may be reformatted as a text for display via a cellular phone orvehicle-mounted display device that is one of the recipient's proxydevices and delivered to that device when it is determined that thedevice is at the specified location. Similarly, voicemails that are tobe delivered to a recipient when a certain team wins a game may bereformatted and transmitted to whatever proxy device the recipient maybe using at the time that the team wins the game. Thus, the voicemailmay be delivered to a cell phone number, a voicemail inbox, an emailinbox as an attachment to an email, or at a work telephone numberdepending on what the recipient is doing at the time the team wins thegame.

In order to determine when delivery conditions are met, the messagedelivery manager 704 may utilize the correlation engine 506 to monitorthe W4 data. Furthermore, a determination that a delivery condition hasbeen met may be possible only through the graphing and identification ofrelationships based on the correlations between IOs and RWEs known tothe W4 COMN, as performed in the correlation engine 506. Therelationships may be determined in response to a request to deliver amessage, triggered by some other input, or may be automaticallydetermined by the correlation engine on a periodic basis and stored forlater use.

FIG. 7B illustrates an alternative embodiments of the aforementioned W4engine 700 present in FIG. 7A. The W4 engine 700 interrelates with acontent server 710. The content server 710, which may be one or multipleservers in one or more locations, is essentially a source of content, beit media content, ad content, graphics, video or text, that a thirdparty is willing to pay a network operator or intermediary to haveembedded in a message as part of an augmented message. In this mannerthe augmented message system can monetize specific types of content viathe W4 engine 700. Examples of content are commercial data, advertisingdata, audio data, video data, literary data, and all other types ofcontent that may be input into a digital message.

The additional content provides opportunities for monetization of theaugmented messaging system described herein. Thus, for example,augmented content might take the form of advertising content or paidmedia placements that, in the context of the system described herein,would be highly targeted to the recipients based upon the context of themessage, the information known about the recipients by the network oravailable to the network, as well as the relationship of sender torecipients or recipients to events described in the message. Thus,augmented messages could contain content by which the systemadministrator or operator that provides the augmented messaging platformcan garner revenue.

Alternative embodiments of the content server 710 may position thecontent server 710 within or as part of the W4 Engine 700. In thisarrangement, the content server 710 would interact directly with themessage enhancement manager 708 during the combining message IO andenhanced content in preparation for delivery.

FIG. 8 illustrates an embodiment of a method for delivering messagesover a network based on social, temporal, spatial and topical data forentities on the network. In the embodiment described below, depending onhow the architecture is implemented, the operations described may beperformed by one or more of the various components, engines and managersdescribed above. In addition, sub-engines may be created and used toperform specific operations in order to improve the network'sperformance as necessary.

As described above, a foundational aspect of the W4 COMN that allows forconditional message delivery is the ongoing collection and maintenanceof W4 data from the RWEs interacting with the network. In an embodiment,this collection and maintenance is an independent operation 899 of theW4 COMN and thus current W4 social, temporal, spatial and topical dataare always available for use in testing of delivery conditions. Inaddition, part of this data collection operation 899 includes thedetermination of ownership and the association of different RWEs withdifferent IOs as described above including prioritization among specificgroups or sub-groups of RWEs and IOs. Therefore, each IO isowned/controlled by at least one RWE with a known, unique identifier onthe W4 COMN and each IO may have many associations with other RWEs thatare known to the W4 COMN.

In the embodiment shown, the method 800 is initiated when a message witha delivery condition is detected by the W4 COMN in a receive deliveryrequest operation 802. Such a request may be generated by software on acomputing device operated by a user, by an automated process or by a“dumb” device such as a cellular phone or a sensor. As discussed above,one or more RWEs may be identified as a sender of the message. Suchidentifications may be made from the data of the message, the source ofthe request or a combination of both. In addition, deductions may bemade concerning a user that is the sender of the message based on W4data for the known device or software senders of the message, aspreviously described.

The delivery request will further identify one or more recipients of themessage. As discussed above, each recipient may be identified by achannel-specific identifier of a proxy RWE of the recipient. Thus,similar to the situation with senders, there may be multiple recipientsassociated with a message. For example, a recipient of an email may beidentified as “bill.smith@yahoo.com”, which is an email address for aelectronic mail account. Using the W4 data, it may be determined thatthe user associated that email address has multiple proxies on the W4COMN including for example the email account identified by“bill.smith@yahoo.com”, a mobile telephone identified with a telephonenumber, a home telephone identified by a different telephone number, atoll payment transponder identified by a transponder identificationnumber, a car identified by a license plate, an internet protocol (IP)address, a business telephone identified by a third telephone number,and a home address identified by one or more physical locationcoordinates or addresses. In an embodiment, requests to deliver amessage to a recipient that is determined to be a proxy for a user (orother RWE such as a business or location) may be interpreted as requeststo deliver the message to the user (or other RWE) that is accessible viathe proxy as discussed below.

In an embodiment, the delivery condition may be part of the deliveryrequest or may be included in an IO (e.g., as data or metadata) thatconstitutes the message. In such a situation, the address string orinformation is associated with the IO that is to be delivered. Suchaddress string or address information may also be a part of the IO to bedelivered. A request may also occur upon detection of an address string,such as for example, a user entering an address string into a field inan email composition screen or speaking an address string into amicrophone on a device.

In an embodiment, the delivery conditions may be designated by thesender of the message, which may be an RWE, typically a user, or an IO,such as a process executing on a computing device. Any suitable way ofselecting and associating the delivery conditions with the message maybe used as long as the W4 COMN can identify the resulting data thatembodies the delivery conditions. For example, for an email message thedelivery conditions may be entered by the user into a delivery optionsinterface provided by the email application, such delivery conditionsthen being stored as metadata of the message: this metadata then isdecoded by the W4 COMN to identify the delivery conditions. For atelephone call, a message delivery system could use a voice or keypaddata entry system to allow the caller to assign or select deliveryconditions to the voice message from a audible menu. Delivery conditionsmay also be automatically generated and added to messages, for exampleby an application such as a parental control application that sendsmessages upon the detection certain activities or content. Other methodsof associating delivery conditions with a message are possible and anysuitable method may be used with the embodiments of the systems andmethods described herein.

In the method 800, the RWEs and IOs may be identified in the deliveryconditions by any identifier, unique or non-unique, communicationchannel-specific or global, as long as the identifier can be resolved bythe W4 COMN to its intended target RWE or IO. Resolving channel-specificidentifiers can be done by correlating the channel-specific identifierwith other W4 data. Non-unique identifiers (e.g., identifiers such as“Mom”, “father”, “Debby”, “Bob”, “Starbucks”) may have to bedisambiguated based on the W4 data known about the sender and themessage to be delivered and any suitable disambiguation method may beutilized for this purpose.

In the embodiment, the message with delivery conditions may beconsidered to be detected when it is received from the sender(s),although the reader will understand that the message may not haveactually been sent at the time of receipt of the delivery conditions. Itis anticipated that under most circumstances that any attributablesender will already be known to the W4 COMN and provided with a uniqueW4 identifier as well as at least one communication channel-specificaddress (which is another form of unique identifier).

As mentioned above, the receive delivery request operation 802 mayinclude receiving an actual IO (e.g., message file or snippet of text)from an RWE or an IO such as a email software being executed by an RWE,the IO to be transmitted as directed by the address string or pointer toan IO at another address or location. The IO may contain data such asthe text or contents of the communication as well as additionalinformation in the form of metadata. The data contained may be evaluatedin order to identify the delivery conditions, additional RWEs associatedwith the message (e.g., people listed in text of a message but that areneither the sender nor a recipient), other IOs (e.g., hyperlinks to IOs,attachments, etc.) contained in the message, and any topics discussed inthe message.

The receive delivery request operation 802 may be considered to occur atany point in the delivery chain within the W4 COMN, e.g., by any one ofthe engines used to conduct IO intake, routing or delivery. For example,depending on how the implementers of the W4 COMN choose to implement thenetwork functions, a message may be received and initially analyzed andinformation routed to the correlation engine and addressing engine byany one of the message intake and generation manager, user profilemanager, message delivery manager or any other engine or manager in theW4 COMN's communication delivery chain.

After detection of delivery conditions associated with a message, thedelivery conditions are analyzed in a delivery condition identificationoperation 804. The delivery condition identification operation 804includes identifying each RWE in the delivery condition and what theactual delivery conditions are with respect to the RWEs. This mayrequire parsing a string containing the delivery conditions or someother analysis of the data or metadata for the message. For example, anIO may be emailed by a sender addressed to a recipient (identified by anemail address) with the delivery condition that the message should onlybe delivered when the recipient is at/with a specified RWE (e.g.,another person, a location such as a park, or a business such as agrocery store, Laundromat, a coffee shop, etc.). In this example, thedelivery condition identification operation 804 will identify therecipient, the specified RWE and a maximum distance or range ofdistances between the two that indicates the delivery condition is met(which may or may not be explicitly provided in the delivery condition).

As discussed above, the recipients and any RWEs in the deliveryconditions may be proxies for users or other RWEs. The identificationoperation 804 includes determining whether the recipient and deliverycondition RWEs are proxies or the actual target of the message. Inaddition if a specified RWE is a proxy, the identification operation 804further includes identifying any RWEs that may be used as proxies forthe specified RWE and for any RWEs for which the specified RWE is,itself, a proxy.

For example, given the relationships described in FIG. 1, a sender of anIO may specify an email address for the user 102 as a recipient and adelivery condition that the recipient (again identified by the emailaddress) and user 144 (which may also be identified by some proxyidentifier such as telephone number or email address) must be together(i.e., co-located). By retrieving the W4 data for the email address, itcan be determined that it is a proxy for the user 102 and that the user102 has many other proxies that could be used to identify the locationof the user 102 including the car 106 and cell phone 104. Theidentification operation 804 would further identify the IOs (e.g., IOs122, 124, 126) from which the current location information may beobtained for each of the identified proxies for the current location ofthe user 102. The process is repeated for the user 144, thus identifyingthe user 144 from the RWE identifier provided in the deliveryconditions, if user 144 has any proxies, and where to obtain currentlocation information for each of the proxies.

The identification operation 804 may distinguish between proxies basedon the data available for the proxy and the delivery condition whenidentifying the proxies to be used to determine if a delivery conditionis met. For example, a work email account for a user may be a proxy forthe user, but if no current location data may be derived from user's useof the email account (e.g., the email account may be accessed frommultiple devices and/or from multiple or any location), the work emailaccount may not be identified as a proxy for the location of the user.If, however, the device the user uses to access the work email accountat any given time can be identified and its location can be determined(e.g., a public computer on a network that the user uses to access hisemail account), then the device could be used as a proxy of the locationof the user by virtue of its current relationship with the user's emailaccount, even though the device may never have been used by the userbefore.

In an embodiment, the W4 engine may assume that for any identified RWEthat is explicitly designated as a proxy for another RWE, that the otherRWE is the intended entity and substitute it for identified RWE. Forexample, if an IO is emailed by a sender addressed to a recipient(identified by an email address) with the delivery condition that themessage should only be delivered when the recipient is at/with aspecified RWE, the IO may be delivered when it is determined that therecipient's cell phone (e.g., a proxy for the recipient, but notnecessarily a proxy for the recipient's email account) is close enoughto the specified RWE's cell phone (e.g., a proxy for the recipient) andthe IO may also be delivered when it is determined that both thespecified RWE and the recipient RWE are attending the same meeting/eventbased on message traffic, financial data (e.g., confirmation of eventticket purchase or detection of concurrent sales made at same location)or smart calendar entries.

The identification of proxies, as discussed above, may be explicit(e.g., designated as proxies by their associated user or RWE) orimplicitly determined based on an analysis of W4 data. As discussedabove, such W4 data may have been collected from messages,communications and IOs previously obtained or handled by the W4 COMN viamany different communication channels and systems, including email andtext-based communication channels as well as any communication channelsthat include audio data including channels that support telephone, voiceover internet protocol (VOIP), and video communications such as videochat.

To determine implicit proxies, the W4 data may be graphed in order todetermine what RWEs are related and how and from this information makeprobabilistic assumptions about the nature of the relationships betweenRWEs. In an embodiment, correlations are made for and between each ofthe RWEs known to the W4 COMN based on the social data, spatial data,temporal data and logical data associated with each RWE. In one sense,the graphing operation may be considered a form of comparing theretrieved social data, spatial data, temporal data and logical data forall RWEs to identify relationships between RWEs and other contextualsimilarities.

It should be noted that the determination of implicit proxies may beperformed each time a delivery condition is tested. This allows for thedynamic determination of the appropriate proxy for any RWE at any time.For example, during the week a corporate car or corporate cell phone maybe considered a good proxy for the location of a recipient; but duringthe weekend a personal cell phone or personal car may be considered abetter proxy for the recipient than the work cell phone.

After the RWEs, including any proxy RWEs, that may be used to confirmthe occurrence of the delivery conditions are identified, the deliverycondition identification operation 804 then identifies one or more datasources for the data necessary to test the delivery conditions. Forexample, if the delivery condition has a location requirement, datasources for the current location of the recipient and the specified RWEswill be identified. If the delivery condition has a temporalrequirement, a system clock or the local time for the recipient and thespecified RWEs may be identified. If the delivery condition has a statusor state-related condition (e.g., a condition based on some identifiedcurrent sensor readings or other condition such as current trafficconditions, current weather conditions, current speed, what an RWE iscurrently doing, current weather forecast, occurrence of a definedevent, etc.), the appropriate data source or sources are identified thatcontain the current information necessary to test these conditions sothat the message can be delivered upon determination that the currentstate of the RWE matches the specified state identified by the deliverycondition.

For example, if the recipient is a user with a cell phone, the cellphone may be identified as the proxy for the current location of therecipient. The identification operation 804 will then identify thelocation of the data source from which the current location of the cellphone may be obtained. Such a data source, for example, may bemaintained by the cellular service provider and accessible through theirnetwork. Alternatively, the cell phone may be provided with a GPSlocator and the current location may be accessible from the cell phoneitself. If the specified RWE is a business, a proxy RWE may not beneeded but a proxy IO that contains information about the businessincluding its current location(s) may be identified.

The delivery condition identification operation 804, as mentioned above,further includes identifying what constitutes the delivery condition(s)being net. For example, if the delivery condition requires an RWE be ata location, a range of distances between the RWE and identified locationthat, as far as the W4 COMN is concerned, will be treated as being “at”the location. Such a range may be predetermined by the operators of theW4 COMN (e.g., “at” is defined as within 10 meters), provided orotherwise selected by the sender as part of the delivery conditions ordynamically determined based on the data available and the precision ofthe location data or the application package or requirements.

If the delivery condition has a temporal requirement, a location mayneed to be identified if the sender designated only a relative time(e.g., Thursday at 6:00 am or Christmas) as opposed to an absolute time(Thursday, Nov. 8, 2007, 6:00 am Mountain Standard Time). The W4 COMNmay, unless otherwise specified, assume all times are local to thesender or the recipient and, when future times are designatedimprecisely, the sender may be prompted for more information or the nextpossible match may be used.

If the delivery condition has a status or state-related condition, theappropriate condition is quantified so that it can be tested using thedata from identified data source or sources. Again, the exact conditionmay have been specified by the sender (e.g., “deliver if winds greaterthan 40 mph detected at home”) or the condition may need to bedetermined from a less specific designations (e.g., “deliver if highwinds detected at home”). For example, a delivery condition based on“heavy traffic” at a location will identify what constitutes heavytraffic in terms of the metrics monitored by and accessible from theidentified traffic sensor. Thus, a threshold or range of values thatwill be considered to be “heavy traffic” when detected may be identifiedor a definition retrieved. As another example, the delivery condition“when the recipient is playing computer games” may be determined byidentifying what software constitutes computer games and determining ifthe software is active at any particular moment. Furthermore, a deliverycondition's quantification may not be the same for every instance of thedelivery condition, but rather may be independently determined based onthe current context and W4 data.

If the identification operation 804 can not adequately identify any ofthe parameters, RWEs and IOs described above or identify the deliveryconditions to the extent sufficient to determine when a deliverycondition has been met, the sender may be notified and asked foradditional clarifying information. For example, in such a situation theW4 COMN may respond by prompting the sender of the message with aquestion such as, “By ‘deliver to debby at the grocery store’, do youmean your wife Deborah or your sister Deborah?” The prompt may includeinformation derived from previous communications or other W4 data toassist the sender in confirming the proper identification of therecipient and delivery condition.

In addition, in an embodiment the delivery conditions may be veryspecifically designated by the sender when the message was created. Forexample, a sender may direct the W4 COMN to deliver a message only tothe recipient via the recipient's cell phone, when the recipient's carand cell phone both are detected near a specified location at the sametime so that when the sender can be certain that the message will not bedelivered when, for instance, the recipient cycles by the gas stationwith the recipient's cell phone or when the sender drives by thelocation in the recipient's vehicle. This allows the sender to be veryspecific in defining delivery conditions, if that is what is desired ornecessary for proper delivery of the message the way the senderintended.

After the delivery condition(s) and recipient(s) have been identified asdescribed above, the delivery conditions are tested in a testingoperation 806. This will include inspecting the necessary data sourcesand comparison of the various data elements (e.g., current location,temperature, state, etc.) as needed to determine if the deliverycondition is met. In an embodiment, testing may include retrieving orrequesting data from the identified data sources. The testing operation806 may require only simple comparisons, e.g., comparing two values,such as current locations, to determine if the values are within aspecified range. The testing operation 806 may be more complicated suchas requiring complex calculations or simultaneous testing of multipleconditions related to a plethora of different RWEs and may includegathering and analyzing data from external sources.

Based on the results of the testing operation 806, a determinationoperation 808 determines if the delivery condition(s) are met or not. Ifthe conditions are not met, then the delivery conditions are retested byrepeating the testing operation 806. By such repeated testing the W4data is monitored for occurrence of the delivery conditions. Theretesting may be done periodically on a fixed schedule or dynamically inresponse to external conditions such as the receipt of new data on theW4 backbone related to RWEs implicated by the delivery conditions. Theretesting may be done in perpetuity until the conditions are met or fora predetermined maximum time period specified by the sender or the W4COMN. If a message cannot be delivered before the maximum time period isreached, the sender may be notified that the message was not delivereddue to the delivery conditions not being met, e.g., “Message notdelivered because Debby did not visit the grocery store within thespecified period.”

If the determination operation 808 determines that the deliveryconditions are met, the message is then transmitted to the recipient(s)in a delivery operation 810. In an embodiment, if the identifiedrecipient is a proxy RWE, the message may be delivered to the proxy RWEregardless of the conditions identified that met the delivery condition.For example, an email may be sent to an identified email address whenthe cell phone of the user associated with that email address isdetected at the delivery condition location. However, in this situationit is possible that the user may not receive the email until later ifthe cell phone is not email-enabled.

Alternatively, the method may select a communication channel and RWE todeliver the message to based on the conditions that triggered thedelivery. For example, if a message is an email and the deliverycondition is the recipient being at a location, upon detection that therecipient's cell phone is at the location the message may be reformattedfor the recipient's cell phone, such as into an IM or SMS message, andtransmitted to the cell phone via the cellular communication networkservicing the cell phone. In such an embodiment, not only the identifiedproxy RWE of a recipient but all other proxies for the recipient (orother RWE) are considered possible delivery routes to the recipient.This allows the W4 message delivery system to select the mostappropriate delivery route/communication channel/proxy combination whenfinally delivering the message, regardless of what deliveryroute/communication channel/proxy was initially identified as therecipient or used by the sender to create and send the message to the W4COMN.

Such a dynamic message delivery system allows the delivery of message byalternative but effective means. For example, based on W4 data the cellphone of a user's best friend may be selected as a proxy for the user(as well as for the best friend) and a message to the user may beautomatically delivered to the best friend's cell phone when deliveryconditions are met and it is determined that the user and the bestfriend's cell phone are co-located. Under these circumstances themessage delivery method can quickly and effectively deliver messages bycommunication channels and to devices that are completely unknown to thesender. As another example, when traveling, email messages for a usermay be automatically delivered to coworkers with the user if the userdoes not have email access.

In addition, in an embodiment the delivery channel and RWE may bespecifically designated by the sender when the message was created. Forexample, a sender may direct the W4 COMN to deliver a message only tothe recipient via the car, when the car is detected near a gas stationso that when the recipient cycles by the gas station with therecipient's cell phone delivery of the message is not triggered.Alternatively, the sender may identify that the W4 COMN dynamicallyselect the communication route and recipient proxy device so that themessage is delivered to the recipient in the manner that is most likelyto get the message to the intended recipient user (as opposed to proxyfor the user) upon occurrence of the delivery conditions.

As described above, the W4 COMN network contains and tracks,continuously, updated information regarding the senders and recipientsthat are RWEs on the W4 COMN. Thus, based on the information known tothe W4 COMN regarding each RWE sender and recipient, a message sent by afirst RWE sender would appear differently when received by each RWErecipient, based upon information known to the W4 COMN and utilized toaugment each message passed through the W4 COMN to each intended RWErecipient. Thus, by way of example, a sender of a message to tworecipients inviting them to dinner at a Japanese restaurant may type ane-mail addressed to recipient one and recipient two that states: “Let'sdo dinner Thursday night at Nobu”. Based upon user profile informationknown to the W4 COMN, recipient one may be known to be an aficionado ofsake. This may be based on explicit user profile information obtainedvia communications related to the subject of sake, or frequent visits towebsites offering information related to sake, profile information ortags associated with a user's profile on social networking sites knownto the W4 COMN, purchase information recorded by the W4 COMN related topurchases of sake at wine shops, or search queries wherein recipient onehas done extensive searching or research related to sake.

Accordingly, the message originally transmitted by sender to recipientone would be received on the W4 COMN, parsed to extract informationconcerning the subject of the message, as well as the identification ofintended recipients. Based on the parsed content and intendedrecipients, and by way of non-limiting example, the W4 COMN wouldextract information related to the recipients and the subject such thatrecipient one would received an invitation to dine at Nobu that alsoincluded information about the restaurant's extensive sake menu,including links to various types of sake featured in the menu, alongwith pricing. Additionally, by way of further non-limiting example, amap to the location of the restaurant could also be included, and a linkto obtain directions from the recipient's home, given that recipient oneis a known RWE on the W4 COMN. Alternatively, the W4 COMN could, inanalyzing information about recipient one, identify the time of theintended dinner meeting as occurring during a time where recipient oneis normally at work, and alternatively provide directions from recipientone's work location.

On the other hand, recipient two may be a vegetarian or have otherspecial meal requirements. This information could be known to the W4COMN in similar manner to the description of the information knownconcerning recipient one's affinity for sake. Accordingly, recipienttwo's message would not contain information relating to sake, butrather, would contain information related to vegetarian items availableon the restaurant's menu, along with a map to the restaurant from adifferent location, since information pertaining to recipient two knownto the W4 COMN, for example from recipient two's calendaringapplication, identifies the night of the proposed dinner as occurringafter recipient two's tennis lesson, and thus, directions from thetennis court to the restaurant would be included in the augmentedmessage.

As in the above example, the augmented message is created through theinformation in the sender's profile available to the W4 COMN, thereceivers' respective profiles, and the content of the message. Theaugmentation of the message will occur, preferably, utilizing the mostcurrent information about the senders and the recipients known to the W4COMN at the time the message is created and intended to be sent. Amessage can be augmented based on a user's declared interests, whichwill determine how the augmented message is ultimately presented. Thevarious users on the network that are identified as senders andrecipients can have messages augmented in as complex or as simple amanner as the information about the sender or recipient that is known tothe system augmenting the message. Thus, an intended recipient that hassubstantial amounts of information such as photos, literary interests,food interests, purchase history, calendar and location information, aswell as a variety of different devices identified with that individual,has a richer opportunity to receive augmented messages than a personwith less information available to the messaging network. Nonetheless,to the extent that the system can derive any declared interest orimplicit interest information from a user's past interactions with thenetwork or systems accessible to the network, such information would beavailable to be utilized as part of the augmentation process.

Additionally, the ultimate format in which the messages received couldbe personalized based upon a variety of factors. Thus, as describedabove, a user RWE can interact with the W4 COMN via a variety ofdevices. Additionally, those devices may be in different locations atdifferent times. If at the time a message is sent by a sender, anintended recipient is on their cell phone, this fact can be known to theW4 COMN, based upon usage information gathered by the cell, orrecognition that the user is logged on to the system via their cellphone and not via their personal computer at the time the message is tobe sent. Accordingly, a further element of context that can be utilizedto augment the message is the actual device that will be utilized by arecipient to receive and view the augmented message. In otherembodiments, the system augments the message differently if it wasintended to be received on a user's cell phone, versus via a TV versusvia a web-browser.

Thus, in the broadest sense, the present disclosure relates to theaugmenting of messages sent by senders to intended recipients, theaugmenting occurring via a communication network that contains, or hasaccess to information regarding the sender and recipients (or recipientsalone), so that each augmented message contains the richest contentpossible, based upon a parsing of the message content, theidentification of the various sender/receiver pairs implicated by themessage, the modeling of social relationships between the senders andrecipients and among the individual recipients, the identification ofsocial structures or other data known or accessible by the networkregarding the recipients, and the utilization of such information tosupplement the original message created by the sender with augmentedinformation that will be of high value to the recipient upon receipt ofthe augmented message.

In some instances, messages may be structured, such as messages thatthemselves contain metadata. In such case, the parsing of the messagecontent would leverage any metadata found in the message, the metadatathen acting as an additional information resource that can be utilizedby the message augmentation network to seek additional content for usein creating the augmented messages to be sent to intended recipients.

Thus, while certain examples above have been given in the context of W4COMN, it is apparent from the disclosure herein that the systems andconcepts disclosed may be utilized in networks other than W4 COMN, suchas for example, in social networks that provide internal messaging ormessage boards, electronic mail systems that have the ability to gatherinformation concerning intended recipients and senders, photo sharingsites, or other network-connected user communities wherein userinformation can be extracted or obtained or stored, either explicitly orimplicitly, for utilization in augmenting messages. In a further broadsense, the disclosure relates to a form of message that may be parsed toextract message content and sender and recipient information. Furtherinformation on intended recipients is then accessed and then analyzed,and the result of the analysis utilized to incorporate other informationinto the message so as to augment the message to contain information notoriginally sent by the sender, but augmented by the system based upondeclared or extrapolated interests or requirements of the recipients.

Continuing one example above, once a message has been parsed and therecipient's information analyzed, the types of information that may beincluded in the augmented message are limited only by the potential sizeconstraints of the message to be send and/or the additional resourcesthat may be available to the network for use in augmented message suchas, in the example above, photographs of the restaurant interior,reviews of the restaurant from a variety of sources available either onthe network or the worldwide web at large, links to related informationsuch as gas stations or parking lots in close proximity to therestaurant, videos containing interior footage, or, as described above,maps that can be tailored based on known information about therecipient's location at the time of message receipt or at the time ofthe intended meeting or subject of the message.

By way of further example, if the intended recipient has a physical needfor information in a different format, such as, for example, Braille, oris physically limited in some way, such as wheelchair-bound, the systemcan augment the message to include a text-to-speech version of themessage content, or information related to public transportation thatcan accommodate wheelchairs and that travel a route proximate theintended meeting place.

The augmentation of messages described herein can be as robust as theinformation available to the system utilized to augment the messages.Thus, as stated earlier, the more information related to intendedrecipients that is stored by or available to the message system, themore resources that are available to be applied by the messaging systemin making determinations as to which augmented content should beincluded in a message. However, irrespective of the amount ofinformation available to the system, the messaging system according thepresent disclosure is intended to operate in a manner such that a set ofrules is defined for parsing message content, and identifyingrecipients. The parsed content is utilized to form a set of variablesassociated with the message, which variables are used to extract orobtain information related to the intended recipients. The informationobtained is matched based on constraints designed into the system, inaccordance with known constraint system methodologies, to map retrieveddata concerning the recipients and identify relationships between theintended recipients and the message content in order to match augmentedor enhanced content with the message content, and provide a new messagethat contains content not present in the original message created by thesender.

For example, the parsed data from a message can be analyzed and datarelated to the parsed message and recipients and obtained by thenetwork, for use and creation of an augmented message. The augmentedmessage may be created by the application of style sheets or templatesthat recognize or resolve message content against available data toyield highly pertinent information to the parsed message data. Thus, ifthe message contains information about a place, maps will be included.If the message, for example, relates to an event, a route or directionscan be included based upon the location of the event and the location atthe recipients, either at the time of the receipt of the message or atthe time of the proposed event, depending on the nature and extent ofinformation available about the recipients.

Thus, for classes of entities and message types, various solutions canapply. The constraint and resolution process takes into account the typeof message and available data and, through a series of iterations,creates an augmented message that, preferably, takes into account thestatus of the sender or receiver and other factors that could influencewhat the augmented communication content should be.

Additionally, conditions such as the type of device upon which theintended recipient will be viewing the message can be taken into accountin creating the augmented message. The number of iterations throughwhich the variables and applied constraints are processed can vary as amatter of design choice, however, it will be recognized that more robustaugmentation of messages can occur when the message parsing is moregranular and the recipient data is more robust.

FIG. 9 illustrates an embodiment of W4 enhanced messaging 900, inaccordance with the present disclosure. (As used herein the termsenhanced and augmented are used interchangeably to represent a messagethat is changed in accordance with the systems and methods describedherein). W4 enhanced messaging 900 occurs during ongoing data collectionby W4 COMN 902, which is continuous, as described above. The process ofW4 enhanced messaging 900 begins when a message request for transmittalof a message sent by a sender is received in step 904. Once this occurs,the message is parsed to extract message content (which forms a W4information object or IO), and W4 data relating to RWEs and IOsassociated with the message are correlated and retrieved in step 906.The message is then graphed and/or mapped to the retrieved W4 data toidentify relationships between the RWEs and IOs that are in associationwith the message IO in step 908. Accordingly, the message is classifiedper a type of message and consequently matched to enhanced content inregards to the message IO in step 910. In step 912, the deliveryconditions are then analyzed to determine if the requirements of W4 COMNprotocol have been reached, and if so, the message proceeds to the nextstep; but if not, there is a feedback loop back to step 908 for furtherW4 data retrieval and identification. The message IO is subsequentlycombined with the enhanced content in preparation for delivery as acomposite IO in step 914, which composite IO is the augmented message.The communication channel and/or format is then selected wherein thecomposite IO is delivered to, or alternatively when sent to therecipient RWEs 916. The process ends at step 918 upon delivery, or itmay end when the augmented message is sent.

FIG. 10A illustrates another embodiment of a system of deliveringmessages over a network to any number of recipients, in an embodimentthat is not implemented in accordance with the W4 COMN. The enhancedmessaging system 1000 includes a transmitter 1002, a messaging system1004 and recipients 1006(1-n). Messaging system 1004 has access to datasources 1003, which may be on the same network as messaging system 1004or may be available on a global network such as the world wide web. Datasources 1003 are sources of information about the sender and recipientsof a message to be sent through the messaging system, as well as sourcesof information about the content of the message. Data sources 1003 maybe social networking sites, subscription services, calendaring systems,websites, music sharing sites, or other network accessible sources ofinformation about the messaging system users and/or message content fromwhich data can be extracted and analyzed for use in developing anaugmented message as described herein.

As discussed in reference to FIG. 10A, a message and/or messages aretransmitted from sender 1002 through the messaging system 1004, forsubsequent delivery to the recipients 1006(1-n). During thiscommunication, the message is parsed in a manner discussed above toextract message content and sender and receiver information or justreceiver information. Messaging system 1004 accesses data sources 1003to find information pertinent to the parsed message, and using thetechniques described above extracts variables that may be associatedwith obtained data to resolve a series of constraints in order to createa new message having augmented content pertaining to each recipients1006(1-n) known or derived profile or characteristics or preferences.Thus, the creation of an enhanced message occurs within the messagingsystem 1004.

Upon the messaging system 1004 receiving the message, elements of themessage are parsed and data related to the recipients and the message,are retrieved. The retrieved data is mapped in order to correlatebetween the appropriate recipients and message content. The messagingsystem 1004 retrieves individual information related to each recipient1006(1-n) for use in augmentation of the message. Accordingly, aspecific enhanced message, which is augmented with data pertinent toeach recipient 1006(1-n), is delivered to each recipient 1006(1-n). Themessaging system 1004 may also have access to individual and/or personalinformation of each recipient 1006(1-n), as provided to the system 1004by the recipients in the form of explicit preference data and socialnetwork affiliations.

FIG. 10B illustrates an alternative embodiments of the aforementionedenhanced messaging system 1000 present in FIG. 10A. The messaging system1004 interrelates with a content server 1008. The content server 1008 isa source, or a representation of multiple sources, of additional typesof content that are available for incorporation into the enhancedmessage produced by the messaging system 1004. Types of such content, byway of non-limiting example, are commercial data, advertising data,audio data, video data, literary data, and all other types ofmonetizable content that may be input into a digital message, forexample advertising content or paid media placements that, in thecontext of the system described herein, would be highly targeted to therecipients 1006(1-n). based upon the context of the message, theinformation known about the recipients by the network or available tothe network, as well as the relationship of transmitter 1002 torecipients 1006(1-n). or recipients to events described in the message,as discussed above.

One non-limiting example of a type of monetized content input in theenhanced message prior to delivery entails the sender 1002 inviting agroup of recipient to play golf. One of the recipients 1006(1-n) has avery strong interest in golf, such interest being derived frominformation discovered through the data retrieved from data sourceshaving information concerning that particular recipient. Accordingly,the messaging system 1004 augments data pertaining to the sport golf,for example: a coupon for golf clubs at a local store, prior to themessage being delivered to the recipient 1006(1-n) having the derivedinterest in golf. The local store then pays the network provider forsuch placement in the augmented message.

Thus, as discussed above, augmented content might take the form ofadvertising content or paid media placements that, in the context of thesystem described above, would be highly targeted to the recipients basedupon the context of the message, the information known about therecipients by the network or available to the network, as well as therelationship of sender to recipients or recipients to events describedin the message. Thus, augmented messages could contain content by whichthe system administrator or operator that provides the augmentedmessaging platform can garner revenue.

In Alternative embodiments, the content server 1008 is part of theenhanced messaging system 1000.

FIG. 11 illustrates a non-limiting example 1100 of augmented messagestransmitted from the sender/transmitter 1102 to three recipients 1104,1106, 1108. Within this example, the message is a party invitation. Asdescribed herein, the message is parsed and information and message typedetermined for each of the three recipients 1104, 1106, 1108, each ofwhom are detected to be utilizing a different type ofcommunication/reception device. Recipient 1104 is on a mobile device,recipient 1106 is using a television set and recipient 1108 is using acomputer system. Based upon each recipients' 1104, 1106, 1108 relevantpersonal data, the message is augmented to include enhanced contentparticular to each of them. These three examples portray the flexibilityof the augmented message delivery options pertaining to alternativerecipients using different devices.

In this example, the sender 1102 sends out a message pertaining to aparty invite at the destination Café Bebo. Per obtained data relevant toeach recipient 1104, 1106, 1108, the original message is augmented withrecipient specific content. Each recipient 1104, 1106, 1108 receives adifferent augmented message modified as appropriate to that recipients'particular known or derived characteristics, likes/dislikes, location,and/or profile, (based on any available data, including topical,spatial, temporal, and/or social data), as well as the communicationchannel and format pertaining to the type of reception device, asdiscussed above.

Recipient 1104 receives the personally enhanced message with augmentedcontent in the form of digital imagery related to the party location,along with a map from the current location of recipient 1104. Per thisexample, recipient 1104 receives the message augmented with photographsof Café Bebo and directions from their current location, which could bederived for example from user profile data or be derived from the sensedlocation of the mobile device of recipient 1104.

Recipient 1106, watching a television set or similar video device, whichmay include typical peripherals which would understood by one ofordinary skill in the art, receives the augmented message containingvideo content and personally relevant data. Recipient 1106 receives adifferent message augmented to include video content pertaining to theparty invite and location, i.e. Café Bebo. Also, as the system hasdetermined based on data about recipient 1106, that recipient 1106 doesnot have a car but is an avid biker, public transportation schedules andbike routes information is provided. Also, as recipient 1106 is known tothe system to be a wine enthusiast, links to wine reviews about winesavailable on the menu at the Café are also included in the augmentedmessage personal to recipient 1106.

Recipient 1108 is logged in on a computer or PC (or similar device), andreceives a different augmented message pertaining to social and topicalcontent relevant to recipient 1108, known to the system as a vegetarian.The personalized augmented message includes vegetarian menu optionsavailable at Café Bebo as extracted from Café Bebo's website, and alsoincludes links to magazine reviews of the vegetarian courses served.Recipient 1108 also receives a video from sender Joe of imagery of CaféBebo.

Thus by way of non-limiting example, the content included in eachaugmented message varies so as to be specific and highly relevant toeach recipient 1104, 1106, 1108 (per this example) and the communicationdevice used.

In other embodiments, it is possible that the augmented message canchange over time as conditions related to one or more recipients changeafter creation of the message. Thus, for example, if a message isaugmented and transmitted to recipient one based on a set of conditionsat the time the message was delivered, and yet the message was not readby recipient one, the system may take note of that fact and thatrecipient one has changed locations. The system can then reprocess themessage based on the new information obtained about recipient one'schanged location, and either send a new message or automatically replacethe old message that was not read with the new message containingupdated information. Thus, for example, if the original messagecontained directions to an intended meeting pace based on the system'sknowledge that recipient one was in location A, and yet recipient onechanges locations to location B prior to reading the first message, asecond message can be created containing directions from location B tothe intended meeting place. Thus, it is possible to provide continuingimproved augmentation of messages by obtaining feedback related to themessages and the state of the messages.

As depicted in FIG. 9A, a step 960 is included to determine if a messagerecipient's status has changed since a message was transmitted, or if amessage reply containing new information was sent. If so, the methodreturns to step 906 to take into account the new information concerningthe message of recipient status to create and send a further augmentedmessage.

Thus, by way of further non-limiting example, and in another exemplarycontext, many social networking sites or professional relationship sitescontain invitations to intended recipients that are not yet members ofthe sender's social network. Such invitations frequently go unanswered.In such a case, if the invitation message was sent as part of theaugmented messaging system described herein, the system could monitorthe fact that the invitation was never responded to and send a furtheraugmented message containing new information that might further inducethe recipient to change their decision not to respond to the invitation.

By further way of non-limiting example, the system could monitor repliesto messages that contain information from a recipient to the sender andother recipients, and augment the reply in similar manner to incorporateadditional content that might be pertinent to the information added bythe recipient that is now the sender of the reply message. Thus, basedupon changes in the status of recipients, or changes in the content ofthe message as result of additions made by recipients, or based uponchanging information relative to the sender, the system can continuouslyaugment and either resend or replace augmented messages as conditions ofthe senders and/or receivers change.

As the W4 COMN continuously monitors communications taking place via orknown to the network, data is created that can be utilized toeffectively identify groups or clusters of communications that aremeaningful in a particular context, or meaningful in particularcircumstances, or meaningful to individual participant RWEs. In otherwords, as IOs are created and processed to extract W4 relationships,messages or communications can be extracted and clustered to form acohesively linked subset of communications that are topically, socially,temporally, and/or spatially related, even if they are independentlyformed and from disparate communication channels. By contrast, intypical communication patterns such as emails, as multiple messages getcreated, sent, responded to and/or forwarded, a “thread” is created thatcontains all communications in a particular chain of communications,which are linked by subject header, communication participants, andmessaging flow. This thread is highly susceptible to breakage however,and fails to include messages about the same topic that might have beensent through other channels such as SMS, MMS, IM, phone call, webcast,photo or video sharing, for example. Standard communication threads arenot only susceptible to breakage due to utilization of differentchannels of communication, but a change in the wording of the subjectheader of a message (which may still be topically or otherwisecoherently related to a previous message with a different subjectheader) and/or a change in the communication handle of the sender and/orreceiver (e.g., email address, IM handle, phone number, etc,) can breakthe messaging thread as well. A cluster of messages as described morefully herein aggregates any number of related communications overdisparate channels into a cohesive unit of related and readilyaccessible information. In other words, by way of non-limiting analogy,a cluster is like a continuous conversation related to a topic, whichconversation can grow more information rich over time.

In a network such as W4 COMN, because communications are analyzed forspatial, temporal, social, and topical information based not only onmessage content but the senders and recipients and channel type, to namea few parameters, a significant amount of data is available for analysisabout messages regardless of their being connected (or not) in aparticular actual thread. Thus, messages (IOs) sent by multiple users(RWEs) from disparate devices (RWEs or proxies) can be parsed asdescribed herein in relation to message content, sender and receiverinformation, or temporal relatedness (sequentially, periodicity,duration, or other measures of temporal similarity) and/or spatialrelatedness (contiguity, containment, or other measures of spatialsimilarity) of IOs or RWEs, to extract data that can be analyzed usingknown clustering algorithms to find related concepts or topics therein.These measures of relatedness can be applied to all components of the W4Communications system (senders, receivers, messages and their metadataand contents, sending and delivery triggers, etc.) and may themselveshave combined and/or second order patterns of similarity, such as thetemporal and/or other patterns of interactions among the components ofthe system (such as cumulative and/or average volume of communications,recency of communications, frequency of communications, etc.), thetopology of the messaging flow and changes therein, temporal patterns ofspatial changes, temporal patterns of topical changes, co-occurrence oftopics, times, and places, etc. These messages are then identified aspart of a cluster of related messages that can be dynamically created,and dynamically modified as new messages are analyzed, which clustersare topically related in a meaningful way to one or more, or all,message senders and recipients. Thus, using the W4 data extractionmethodologies described herein, messages passed through or known to theW4 COMN can be logically and meaningfully clustered into actionablegroups of related messages. The boundaries of the cluster are set usingthe W4 data related to the candidate messages being analyzed, andparameters can be set to draw the cluster boundaries using knownclustering algorithms.

For example, all messages related to a particular event might becandidate messages for a cluster, yet some messages might fall outsidecertain boundary parameters, either because the message is too early orto late in relation to the event, or because the sender is too far fromthe event, or because the sender is known only to one other person amongthe senders and receivers of the messages in the cluster, or because themessage mentions a different context for the event (e.g., parking at theevent, while all other messages relate to dining at the event). Themessages utilized to form clusters, in an embodiment, may be augmentedmessages as described above, and the augmentation process describedabove can utilize information related to messages associated withclusters in further augmenting messages created by or to be received byRWEs associated with a particular cluster, or with IOs related to aparticular cluster.

By way of non-limiting example, assume that W4 RWE “Jerry” is attendingthe global “ZBX conference” in Las Vegas. Jerry sends multiple messagesto multiple people via email, SMS, and voice calls on his cell. Some ofhis emails and calls relate to a speech he is giving at the conference.Some of his texts and emails relate to a dinner he is trying to set upat the conference, and some of his emails, texts and calls relate to around of golf he is trying to organize. Of the three events, somerecipients receive messages related to all three, others only two of theevents, others only one of the events. Because the W4 COMN monitors andparses all of Jerry's messages (IOs) and has knowledge of the RWErecipients and their locations and interrelationships, the network canextract and analyze the information in and about Jerry's messages andorganize them into meaningful clusters such as “Jerry ZBX speech”,“Jerry ZBX dinner” and “Jerry ZBX golf”. The data used to build theseclusters comes from the messages themselves as well as data available onor to the W4 COMN that is related to the message content, senders andreceivers, and any related triggers. The data may be analyzed usingknown algorithms (e.g., K-means clustering, hierarchical clustering,etc.) to cluster related objects in an n-dimensional space, whosedimensions represent W4 metadata, content data, and may also includecomposite and/or second order features upon which to cluster. Thealgorithm can be adjusted in accordance with known techniques tooptimize the distance between objects based on unsupervised machinelearning principles or supervised machine learning techniques, whetherby system operators or by actual user feedback (for example dragging amessage into or out of a cluster can provide information to be used bythe algorithm to fine tune it). Thus, known algorithms incorporatingfeedback techniques to reinforce and improve the quality of the createdcluster are preferably utilized in embodiments, although embodimentswithout such an enhancement are also contemplated as apparent from theteaching herein.

Clusters can be contained in other clusters, or overlap with otherclusters, or be separated or unique from other clusters. Continuing withthe above example, Jerry's golf cluster could contain Jerry's dinnercluster but only partially overlap with Jerry's speech cluster.

Clusters are also to an extent personal, but can be shared among users,either by permission or convention. For example, Jerry's ZBX golfcluster might be shared by the members of the group that is golfing, butJerry can chose to limit access to his Jerry ZBX speech cluster. Inembodiments, clusters that share the same content can be differentlylabeled depending on the owner of the cluster. So a cluster for Mary'sZBX golf cluster that contains the same or virtually all of the same IOsas Jerry's ZBX golf cluster would appear to Mary as Mary's ZBX golfcluster, and as discussed further below regarding the cluster becomingan actionable item, may cause different data to be displayed or actionsto be performed when the cluster is acted upon by Mary, due to Mary'sdiffering social or temporal or spatial relationships to the RWEs thatare related to the IOs associated with Mary's ZBX golf cluster.

It is preferable, but not necessary, that the cluster itself becomes anactionable IO or super IO made up of many IOs, which when applied toother contexts can provide rich and meaningful information. Withreference to the above example, assume Jerry is trying to locate thepeople (RWEs) associated with Jerry's ZBX golf cluster to estimate whenthey will arrive at the golf course and to set up teams, and assumeJerry is using a device and his golf cluster is represented by aninformation object on a display, such as a draggable window that islabeled “Jerry's ZBX golf”. In one example of how a cluster can beactionable, if Jerry drags his golf cluster to a map of Las Vegas, thelocation of the people in the cluster can be displayed on the map attheir then-present location as extracted by the W4 COMN (from cell phonelocation or other sensor data for example), along with information abouttheir interrelationships as extracted from social network data, so Jerrycan set up teams based who knows who and when they will arrive.

Another example of the cluster being actionable is for items in thecluster to be selectable, that is, active items (IOs representing RWEscontained within and/or related to the cluster) that are clickable orselectable in a user interface. By clicking on or otherwise selecting anitem in a cluster, other items related to that item can appear, so thatrelated entities or information objects can be readily identified.Moreover, because the system takes into account the time and place inwhich the action is occurring, the action that results from theinteraction with a cluster can change based on the time or location ofthe person interacting with the cluster. Thus if it is a week before thegolf outing and Jerry clicks on his golf cluster, Jerry might get a listof who has responded to invitations to play, since the system knows theactual date for the golf game is a week away and Jerry has been sendingnumerous email invitations. On the other hand, as described above,interacting with the cluster on the day of the outing would yield adifferent result, such as the map described above or a list of inviteesthat need to rent golf clubs.

Not only can the W4 components of a cluster (IOs representing RWEs suchas people, places, times, objects, topics, and events) can be madeactionable as an entire cluster, but individual W4 components of acluster and/or subsets of W4 components may be made actionable as well.

Because the W4 COMN has access to so much rich data concerning the RWEsand IOs that are related to any cluster, the system can optionallyprovide a list of choices to a user upon interaction with a cluster tobetter inform the system about the action to take. Thus, continuing withthe example above, if Jerry clicks on his ZBX golf cluster, the systemmay return a list of options from which Jerry can select to betterinform the system about the information Jerry needs from the cluster. Byway of non-limiting example, the system would return prompts like“invitees”, “maps”, “contacts” “nearby restaurants” or “weather” forJerry to select from, and return different information according to eachselection.

Additionally, other information available to the W4 COMN and related toitems in a cluster can advise the cluster building algorithm to define acluster as it is being created or to refine an existing cluster.Communications made through other channels, for example non-W4 mailservices or IM services, could be analyzed by the system once theybecome known to the system, by hitting an inbox or message queue forexample, and that information can be used by the system for refinementpurposes, for example by making a connection between a non-W4 mailaddress and a third party mail address, thus disambiguating andaugmenting the representation of RWEs known to the system. Inparticular, while embodiments have been described in relation to a W4COMN, the systems, functions and methods described herein can be appliedto other message networks, such as depicted ion FIGS. 10A and B, orlegacy messages of other networks can be imported into the W4 COMN foranalysis and inclusion.

Thus in the context of the above example, assume RWE “Jim” is mentionedin a message relating to golf. That alone may be insufficient for thealgorithm to include Jim in Jerry's golf cluster. But assume Jim'ssocial network data available to or on the W4 COMN identifies Jim as anavid golfer. Such additional information informs the algorithm toinclude Jim as a member of Jerry's golf cluster.

As information from messages is parsed and related data extracted andanalyzed and clusters formed, the system must determine how to summarizeand label the cluster contents. Standard summarization techniques can beused to summarize the contents of a cluster, yet the cluster needs to belabeled. One methodology for creating a label for a cluster is toutilize known methods to find representative terms found in the messagesin the cluster. One known method is term frequency inverse documentfrequency (TFIDF). Of course such a method can be augmented by otherdata or limits, such as for example temporal limits, so even though agood cluster label might be “Jerry's golf” as identified using TFIDF, abetter label might be “Jerry's Saturday golf” if Jerry has more than oneround scheduled over multiple days. Other methods of conceptualclustering combined with natural language syntax algorithms can convertthe representative W4 data of a cluster into a linguisticallyintelligible label for the cluster. These summarization and labelingalgorithms also can utilize information about the hierarchical and/orother relationships of the clusters when determining the best label forany given cluster,

As described herein, opportunities for monetization of the clusteringtechniques shown and described herein exist. In a similar manner asdescribed above in the context of augmented messages, content that thirdparties are willing to pay to have included in clusters can be madeavailable to the system either as part of the W4 COMN or as externalcontent sources accessible to the W4 COMN. As a result of thecorrelation function that identifies relationships between messages andrelated IOs and RWEs, and in embodiments the summarization and labelingfunctions, a great deal of relevant information is derived about acluster. This information forms the basis for matching the relevantinformation to similarly relevant content that a third party is willingto pay to have included in or available to the cluster. By way ofcontinuing example, as part of the building of Jerry's ZBX golf cluster,the system can recognize, analyzing contextual information in Jerry'smessages as described herein, and include a special offer from the golfcourse that becomes apparent to Jerry when he takes action on thecluster, such as checking invitee status as described above.Alternatively, the system can recognize other potential recipients foraugments messages as described herein based upon the relationship of thepotential recipient to Jerry's golf cluster.

With reference to FIG. 12, a flowchart is depicted for describing anexample of a method of an embodiment for creating message clusters asdescribed herein. In step 1202, ongoing data collection in the W4 COMNis an ongoing process. As messages pass through or become known to theW4 COMN in step 1204, they are parsed and analyzed to retrieve datarelated to the RWEs and IOs associated with the message, in step 1206.In step 1208, and as described in detail in other sections herein, theretrieved data is graphed or mapped so that relationships among the RWEsand IOs identified as related to the parsed message(s) can be correlatedand collected for association with the message. In step 1210, using therelationships identified in the prior steps, related messages areidentified based upon the identified relationships between RWEs and IOsassociated with the message. In step 1212, clusters of messages areformed based upon the relationships between messages identified in step1210, in accordance with rules for building clusters that areestablished as a matter of system design and implementation, and takeinto account such considerations as, by way of non-limiting example, adegrees of relatedness computation, user preferences or constraints, orother temporal, social or spatial relationships that can be identifiedas variables and resolved using a rule set, template, or otherconstraint resolution technique.

In step 1214 the contents of the messages in the cluster are summarizedusing known summarization techniques to establish a corpus of content ofthe cluster that can be further analyzed or refined. By way of example,steps 1210 through 1214 can be iterative to the extent that additionalpasses yield higher confidence results. In step 1216, the messagecontent, or preferably the summarization, is analyzed to determine a“label” or identifying name, graphic, symbol or icon for the clusterthat may be viewed and interacted with by the user on a RWE device suchas a personal computer, PDA or smart phone.

Once a label is created and viewable, and with reference to FIG. 13, thelabel may be displayed on a device display as a user interface element,in step 1310. As described elsewhere herein, a user may interact withthe label, by, for example, clicking or dragging, to perform a furtheraction or seek further information, in step 1312, depending on theaction selected, or upon analysis of the action by the system, the usermay be provided with options to provide further feedback prior to orafter the action being performed. If no such option is presented, or ifno response is received, as detected in step 1314, in step 1316 theaction is performed based upon an analysis of relationships between RWEsand IOs associated with the cluster.

If in step 1318 user feedback is detected, the action selected isperformed based upon an analysis of relationships between RWEs and IOsassociated with the cluster and the user feedback.

With reference to FIG. 7C, additional components of the W4 engine 700are depicted for carrying out the processes described herein relative tothe creation of and interaction with clusters. Components not discussedbut depicted are as discussed above in relation to FIGS. 4-6 and 7A and7B. As described above, messages (or other communications, referred tofor simplicity herein as messages) are parsed and analyzed andcorrelations established between the message content and senders andreceivers and other data available to the system to establishrelationships between messages and RWEs and IOs in the W4 COMN. Asrelated messages are identified (using techniques described herein, suchas for example an n-dimensional space model or vector model), a clusterbuilding engine 730, which may perform the functions of identifyingrelated messages or may do so in cooperation with attribution engine 504and correlation engine 506 described herein, collects the relatedmessages and forms and tracks clusters of these related messages.

As the cluster forms, or in alternate embodiments after a cluster isformed, a summarization engine 730 summarizes the contents of themessages in a cluster using standard summarization techniques. Once thecluster is formed and summarized, a labeling engine 740 analyzes thecluster contents to derive a label for the cluster that isrepresentative and contextually meaningful. This label may then bedisplayed on a user device in the form of an interface element orgraphic, text, icon or shape, for example, to visually represent thecluster to the user and to provide an element through which the label,and in turn the cluster, can be interacted with by the user.

As described herein, the system can be configured to make the clustersand their components actionable by the user, or to accept and processuser feedback related to desired actions on the cluster. Cluster actionengine 750 monitors the systems for actions taken on or with clusterlabels and, as appropriate to the detected action, will request furtherfeedback from the user or process the action, depending on whether suchfeedback is detected.

Content server 710, which represents one or multiple content sourcessuch as advertisement servers or other media or content suppliers fromwhich payment can be had, as described elsewhere, are connected to, orin embodiments part of, W4 engine 700. The function of identifyingopportunities for monetizeable content placement and communicating withcontent server 710 to obtain such content can be performed in clusterbuilding engine 730 or cluster actions engine 750, or can be adistributed function performed among a combination of other components,such as correlation engine 506.

FIGS. 14A-14C depict one non-limiting example of a user having clustersrelated to various upcoming events, including the ZBX conference relatedevents described in examples herein, as well as other events, forexample, vacation and golf at Pebble Beach. As seen in FIG. 14A, thecluster labeled ZBX 1402 has sub-clusters labeled ZBX dinner 1404 andZBX keynote 1406, depicting that clusters can be hierarchicallyarranged. The hierarchical arrangement of the cluster provides theparticipants in the cluster an organized portrayal of related events. Inthis embodiment, the cluster ZBX 1402 and sub-clusters ZBX dinner 1404and ZBX keynote 1406 have already been created by the labeling engine740 and delivered to a user's device via the message delivery manager704, therefore they are being displayed on a user's device interfaceelement 1408 (i.e., computer screen, cellular phone screen, PDA screen,etc.). In FIG. 14B, the user determines to take action upon the ZBXdinner cluster 1404 by dragging and dropping it to a map 1410, whichdepicts the city in which the subject dinner is to occur, in order tobetter identify a location and preferences of the people (RWEs) relatedto the ZBX dinner cluster 1404. The dragging and dropping actions, whichare commonly know techniques, here signal the system to correlate theZBX dinner cluster 1404 with the map 1410. As discussed above inreference to FIG. 13, the user may perform further actions or seekfurther information by further interaction with a cluster label. In FIG.14C, in response to the dragging and dropping, the ZBX dinner cluster1404 may be expanded to display to the user the participants of thedinner and additional data pertaining to each user and/or the clustercontent. As in this embodiment, each of the participants in ZBX dinnercluster 1404 have preferred dinner location(s) 1412 which may be derivedfrom each of their profiles. The cluster action engine 750, as describedin one embodiment above, would perform further analysis and utilizesocial network and preference information to identify the restaurantsand food types enjoyed by the proposed dinner participants in order toassist the user in picking a restaurant for the dinner, as seen in FIG.14C.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible. Functionality may also be, inwhole or in part, distributed among multiple components, in manners nowknown or to become known. Thus, myriad software/hardware/firmwarecombinations are possible in achieving the functions, features,interfaces and preferences described herein. Moreover, the scope of thepresent disclosure covers conventionally known manners for carrying outthe described features and functions and interfaces, as well as thosevariations and modifications that may be made to the hardware orsoftware or firmware components described herein as would be understoodby those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure. Numerous other changes may bemade that will readily suggest themselves to those skilled in the artand which are encompassed in the spirit of the disclosure(s) disclosed.

What is claimed is:
 1. A method comprising: receiving, at a computingdevice executing on a W4 Communications Network (W4 COMN), a pluralityof messages for delivery over a network, each message comprisinginformation respective to an identity of at least one recipient;parsing, via the W4 COMN computing device, each message, andidentifying, based on said parsing by the W4 COMN computing device,message content from each of said plurality of messages; extracting, viathe W4 COMN executing an extraction application, said identified messagecontent from each of said plurality of messages; searching the W4 COMN,via the W4 COMN computing device, for W4 data comprising social data,spatial data, temporal data and logical data related to each messagerecipient and W4 data comprising social data, spatial data, temporaldata and logical data relating to the extracted message content;determining, via the W4 COMN computing device, recipient data availableto the W4 COMN for each received message based on the W4 data for eachmessage recipient and the W4 data for each extracted message content;augmenting, via the W4 COMN computing device, additional contentcorresponding to the determined recipient data to the extracted messagecontent of each received message; creating, via the W4 COMN computingdevice, a new message based on said augmentation such that each createdmessage comprises said additional content and respective extractedmessage content; determining, via the W4 COMN computing device, aboundary for grouping said created messages, said boundary determinationcomprising analyzing the extracted W4 data of the extracted messagecontent in the created messages, and based on said analysis, correlatinga relationship between the social data, spatial data, temporal data andlogical data of the extracted content of each created message, saiddetermined boundary set according to the correlated relationship betweenthe W4 data of each created message; forming, via the W4 COMN computingdevice, a message cluster based on said determined boundary, saidformation of the message cluster comprising analyzing the extractedcontent of each created message, and based on said analysis, determiningwhether the W4 data of said extracted content falls within saiddetermined boundary, said formation further comprising forming saidmessage cluster based on those created messages that fall within saiddetermined boundary; and communicating, via the W4 COMN computingdevice, the created messages in said message cluster to each identifiedrecipient over the W4 COMN.
 2. The method of claim 1, wherein saidadditional content augmented to each message is different for eachrecipient.
 3. The method of claim 1, further comprising: identifying asender of each received message, said identifying comprising identifyingsender data on the W4 COMN comprising information specific to eachsender; and augmenting each newly created message with second additionalcontent based on said identified sender information.
 4. The method ofclaim 1, further comprising: monitoring, over the W4 COMN, the recipientdata associated with said communication to a time each recipient openssaid communicated message, said monitoring comprising determining whensaid recipient data for each message has changed from said communicationto the opening time, wherein, when said recipient data has changed,augmenting said message with third additional content based on saidmonitored recipient data and removing said additional information, andwherein, when said recipient data has not changed, maintain additionalinformation in said message.
 5. The method of claim 4, wherein saidmonitoring further comprises: determining a state of each message; andupdating the message cluster with said state information for eachmessage.
 6. The method of claim 5, wherein when said message state hasbeen determined to have changed prior to said recipient opening saidmessage, removing said message from said message cluster, createdanother new message that comprising said third additional contentaugmented to said message content.
 7. The method of claim 1, whereinsaid recipient data corresponds to a user profile of a recipient, saiduser profile comprising said information which includes known andderived information associated with said recipient.
 8. The method ofclaim 1, wherein said boundary represents a constraint for said clusterbased on message content of said messages, wherein said constraint is inaccordance with a context and pattern derived from said message content.9. A non-transitory computer-readable storage medium tangibly encodedwith computer-executable instructions, that when executed by at leastone processor associated with a computing device executing on a W4Communications Network (W4 COMN), perform a method comprising: receivinga plurality of messages for delivery over a network, each messagecomprising information respective to an identity of at least onerecipient; parsing each message, and identifying, based on said parsingby the W4 COMN computing device, message content from each of saidplurality of messages; extracting, using an extraction applicationexecutable on the W4 COMN, said identified message content from each ofsaid plurality of messages; searching the W4 COMN for W4 data comprisingsocial data, spatial data, temporal data and logical data related toeach message recipient and W4 data comprising social data, spatial data,temporal data and logical data relating to the extracted messagecontent; determining recipient data available to the W4 COMN for eachreceived message based on the W4 data for each message recipient and theW4 data for each extracted message content; augmenting additionalcontent corresponding to the determined recipient data to the extractedmessage content of each received message; creating a new message basedon said augmentation such that each created message comprises saidadditional content and respective extracted message content;determining, a boundary for grouping said created messages, saidboundary determination comprising analyzing the extracted W4 data of theextracted message content in the created messages, and based on saidanalysis, correlating a relationship between the social data, spatialdata, temporal data and logical data of the extracted content of eachcreated message, said determined boundary set according to thecorrelated relationship between the W4 data of each created message;forming, via the W4 COMN computing device, a message cluster based onsaid determined boundary, said formation of the message clustercomprising analyzing the extracted content of each created message, andbased on said analysis, determining whether the W4 data of saidextracted content falls within said determined boundary, said formationfurther comprising forming said message cluster based on those createdmessages that fall within said determined boundary; and communicatingthe created messages in said message cluster to each identifiedrecipient over the W4 COMN.
 10. The non-transitory computer-readablestorage medium of claim 9, wherein said additional content augmented toeach message is different for each recipient.
 11. The non-transitorycomputer-readable storage medium of claim 9, further comprising:identifying a sender of each received message, said identifyingcomprising identifying sender data on the W4 COMN comprising informationspecific to each sender; and augmenting each newly created message withsecond additional content based on said identified sender information.12. The non-transitory computer-readable storage medium of claim 9,further comprising: monitoring, over the W4 COMN, the recipient dataassociated with said communication to a time each recipient opens saidcommunicated message, said monitoring comprising determining when saidrecipient data for each message has changed from said communication tothe opening time, wherein, when said recipient data has changed,augmenting said message with third additional content based on saidmonitored recipient data and removing said additional information, andwherein, when said recipient data has not changed, maintain additionalinformation in said message.
 13. The non-transitory computer-readablestorage medium of claim 12, wherein said monitoring further comprises:determining a state of each message; and updating the message clusterwith said state information for each message.
 14. The non-transitorycomputer-readable storage medium of claim 13, wherein when said messagestate has been determined to have changed prior to said recipientopening said message, removing said message from said message cluster,created another new message that comprising said third additionalcontent augmented to said message content.
 15. The non-transitorycomputer-readable storage medium of claim 9, wherein said recipient datacorresponds to a user profile of a recipient, said user profilecomprising said information which includes known and derived informationassociated with said recipient.
 16. The non-transitory computer-readablestorage medium of claim 9, wherein said boundary represents a constraintfor said cluster based on message content of said messages, wherein saidconstraint is in accordance with a context and pattern derived from saidmessage content.
 17. A computing device executing on a W4 CommunicationsNetwork (W4 COMN) comprising: a processor; a non-transitorycomputer-readable storage medium for tangibly storing thereon programlogic for execution by the processor, the program logic comprising:logic executed by the processor for receiving a plurality of messagesfor delivery over a network, each message comprising informationrespective to an identity of at least one recipient; logic executed bythe processor for parsing each message, and identifying, based on saidparsing by the W4 COMN computing device, message content from each ofsaid plurality of messages; extracting, using an extraction applicationexecutable on the W4 COMN, said identified message content from each ofsaid plurality of messages; logic executed by the processor forsearching the W4 COMN for W4 data comprising social data, spatial data,temporal data and logical data related to each message recipient and W4data comprising social data, spatial data, temporal data and logicaldata relating to the extracted message content; logic executed by theprocessor for determining recipient data available to the W4 COMN foreach received message based on the W4 data for each message recipientand the W4 data for each extracted message content; logic executed bythe processor for augmenting additional content corresponding to thedetermined recipient data to the extracted message content of eachreceived message; logic executed by the processor for creating a newmessage based on said augmentation such that each created messagecomprises said additional content and respective extracted messagecontent; logic executed by the processor for determining a boundary forgrouping said created messages, said boundary determination comprisinganalyzing the extracted W4 data of the extracted message content in thecreated messages, and based on said analysis, correlating a relationshipbetween the social data, spatial data, temporal data and logical data ofthe extracted content of each created message, said determined boundaryset according to the correlated relationship between the W4 data of eachcreated message; logic executed by the processor for forming a messagecluster based on said determined boundary, said formation of the messagecluster comprising analyzing the extracted content of each createdmessage, and based on said analysis, determining whether the W4 data ofsaid extracted content falls within said determined boundary, saidformation further comprising forming said message cluster based on thosecreated messages that fall within said determined boundary; and logicexecuted by the processor for communicating the created messages in themessage cluster to each identified recipient over the W4 COMN.
 18. Thecomputing device of claim 17, further comprising: logic executed by theprocessor for monitoring, over the W4 COMN, the recipient dataassociated with said communication to a time each recipient opens saidcommunicated message, said monitoring comprising determining when saidrecipient data for each message has changed from said communication tothe opening time, wherein, when said recipient data has changed,augmenting said message with third additional content based on saidmonitored recipient data and removing said additional information, andwherein, when said recipient data has not changed, maintain additionalinformation in said message.
 19. The computing device of claim 18,wherein said monitoring further comprises: logic executed by theprocessor for determining a state of each message; and logic executed bythe processor for updating the message cluster with said stateinformation for each message, wherein when said message state has beendetermined to have changed prior to said recipient opening said message,removing said message from said message cluster, created another newmessage that comprising said third additional content augmented to saidmessage content.
 20. The computing device of claim 17, furthercomprising: logic executed by the processor for identifying a sender ofeach received message, said identifying comprising identifying senderdata on the W4 COMN comprising information specific to each sender; andlogic executed by the processor for augmenting each newly createdmessage with second additional content based on said identified senderinformation.